{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  Import required python modules\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please provide a full-path output directory:\"Y:\\LRMF\\BiodiversityBAP\\Visuals\\Colorado\"\n"
     ]
    }
   ],
   "source": [
    "##  Define existing output location\n",
    "##  i.e. Y:\\LRMF\\graphics\\Columbia\n",
    "output = input(\"Please provide a full-path output directory:\")\n",
    "##output = \"Y:\\\\LRMF\\\\scratch\\\\Colorado\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please provide a full-path input file:\"Y:\\LRMF\\BiodiversityBAP\\Inputs\\Colorado_biodiv.csv\"\n"
     ]
    }
   ],
   "source": [
    "##  Ask for input file, full path\n",
    "##  i.e. Y:\\LRMF\\R_tables\\columbia_river_orig.csv\n",
    "inputFile = input(\"Please provide a full-path input file:\")\n",
    "##inputFile = \"Y:\\\\LRMF\\\\R_tables\\\\colorado_river_orig.csv\"\n",
    "\n",
    "data = pd.read_csv(inputFile,header=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  define variable as count of columns in table.  Provides \"stopping\" point for calculations after indices have been added\n",
    "last = len(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>PLKF</th>\n",
       "      <th>RBSK</th>\n",
       "      <th>RBTT</th>\n",
       "      <th>RDSN</th>\n",
       "      <th>RTCB</th>\n",
       "      <th>SDBS</th>\n",
       "      <th>SPDC</th>\n",
       "      <th>TFSD</th>\n",
       "      <th>WLYE</th>\n",
       "      <th>YLBH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.913462</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.427586</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.271242</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.687500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT  ...   PLKF  RBSK       RBTT  RDSN  RTCB  SDBS      SPDC  TFSD  \\\n",
       "0  0.048077  ...      0     0  10.913462     0     0     0  0.019231     0   \n",
       "1  0.013793  ...      0     0   6.427586     0     0     0  0.013793     0   \n",
       "2  0.019608  ...      0     0   6.271242     0     0     0  0.016340     0   \n",
       "3  0.000000  ...      0     0   5.687500     0     0     0  0.000000     0   \n",
       "4  0.000000  ...      0     0  11.500000     0     0     0  0.000000     0   \n",
       "\n",
       "   WLYE  YLBH  \n",
       "0     0     0  \n",
       "1     0     0  \n",
       "2     0     0  \n",
       "3     0     0  \n",
       "4     0     0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## add new column\n",
    "data[\"Margalef\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  for each row calculate Margalef Species Richness Index\n",
    "for i in range(len(data)):\n",
    "    data.loc[i,'Margalef'] = (len(data.iloc[i][4:last][data.iloc[i][4:last]>0])-1)/math.log(sum(data.iloc[i][4:last][data.iloc[i][4:last]>0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>RBSK</th>\n",
       "      <th>RBTT</th>\n",
       "      <th>RDSN</th>\n",
       "      <th>RTCB</th>\n",
       "      <th>SDBS</th>\n",
       "      <th>SPDC</th>\n",
       "      <th>TFSD</th>\n",
       "      <th>WLYE</th>\n",
       "      <th>YLBH</th>\n",
       "      <th>Margalef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>10.913462</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.87598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6.427586</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.10716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6.271242</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.15943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5.687500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT    ...     RBSK       RBTT  RDSN  RTCB  SDBS      SPDC  TFSD  \\\n",
       "0  0.048077    ...        0  10.913462     0     0     0  0.019231     0   \n",
       "1  0.013793    ...        0   6.427586     0     0     0  0.013793     0   \n",
       "2  0.019608    ...        0   6.271242     0     0     0  0.016340     0   \n",
       "3  0.000000    ...        0   5.687500     0     0     0  0.000000     0   \n",
       "4  0.000000    ...        0  11.500000     0     0     0  0.000000     0   \n",
       "\n",
       "   WLYE  YLBH  Margalef  \n",
       "0     0     0   2.87598  \n",
       "1     0     0   3.10716  \n",
       "2     0     0   3.15943  \n",
       "3     0     0   0.57169  \n",
       "4     0     0         0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## get unique river segment names\n",
    "segments = data.RiverSeg.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  get river name\n",
    "river = data.iloc[0][\"River\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'module' object has no attribute 'to_rgba'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-e6c1ca44db9e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"\\\\\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mriver\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"_margalef.png\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'tight'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[1;31m##  display figure\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda2\\lib\\site-packages\\matplotlib\\pyplot.pyc\u001b[0m in \u001b[0;36mshow\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m    242\u001b[0m     \"\"\"\n\u001b[0;32m    243\u001b[0m     \u001b[1;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    245\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda2\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.pyc\u001b[0m in \u001b[0;36mshow\u001b[1;34m(close, block)\u001b[0m\n\u001b[0;32m     37\u001b[0m             display(\n\u001b[0;32m     38\u001b[0m                 \u001b[0mfigure_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m                 \u001b[0mmetadata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m             )\n\u001b[0;32m     41\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda2\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.pyc\u001b[0m in \u001b[0;36m_fetch_figure_metadata\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    172\u001b[0m     \u001b[1;34m\"\"\"Get some metadata to help with displaying a figure.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    173\u001b[0m     \u001b[1;31m# determine if a background is needed for legibility\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 174\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_facecolor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    175\u001b[0m         \u001b[1;31m# the background is transparent\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    176\u001b[0m         ticksLight = _is_light([label.get_color()\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda2\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.pyc\u001b[0m in \u001b[0;36m_is_transparent\u001b[1;34m(color)\u001b[0m\n\u001b[0;32m    193\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    194\u001b[0m     \u001b[1;34m\"\"\"Determine transparency from alpha.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 195\u001b[1;33m     \u001b[0mrgba\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    196\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mrgba\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m.5\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'module' object has no attribute 'to_rgba'"
     ]
    }
   ],
   "source": [
    "##  define color list\n",
    "colors = ['b', 'g', 'r', 'k', 'c', 'm', 'y']\n",
    "##  define index for iterating through color list\n",
    "index = 0\n",
    "\n",
    "##  for each river segment\n",
    "for segment in segments:\n",
    "    ##  subset df to non-zero values for the current river segment\n",
    "    segDF = data.loc[data['RiverSeg'] == segment]\n",
    "    segDF = segDF[segDF[\"Margalef\"]>0]\n",
    "\n",
    "    ## sort based on year\n",
    "    segDF = segDF.sort_values('Year')\n",
    "    ## define x,y for plotting\n",
    "    x = segDF[\"Year\"]\n",
    "    y = segDF[\"Margalef\"]\n",
    "    ## change name of y to Riv Seg for legend\n",
    "    y.name = segment\n",
    "    ##  build graph...\n",
    "    ##  plot segment x vs y assigning color based on index\n",
    "    plt.plot(x,y,colors[index])\n",
    "    ##  locate legend\n",
    "    plt.legend(loc=(1.05,0.2))\n",
    "    index += 1\n",
    "\n",
    "##  update title\n",
    "plt.title(\"Margalef Index, \" + river + \" River Segments\")\n",
    "##  label x axis\n",
    "plt.xlabel('Year')\n",
    "##  label y axis\n",
    "plt.ylabel('Margalef Index')\n",
    "##  force x axis to integer values, increment by 1 year\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "##  rotate year labels 90 degrees\n",
    "plt.xticks(rotation=90)\n",
    "##  advance color index    \n",
    "    \n",
    "    \n",
    "##  save figure\n",
    "plt.savefig(output + \"\\\\\" + river + \"_margalef.png\", bbox_inches='tight',dpi=300, size=(2000,2000))\n",
    "##  display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## add new column\n",
    "data[\"SWI_2\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  for each row calculate Shannon-Wiener Log(2) Index\n",
    "for i in range(len(data)):\n",
    "    swi = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            swi += -(data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])) * math.log((data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])),2)\n",
    "    data.loc[i,'SWI_2'] = swi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>RBTT</th>\n",
       "      <th>RDSN</th>\n",
       "      <th>RTCB</th>\n",
       "      <th>SDBS</th>\n",
       "      <th>SPDC</th>\n",
       "      <th>TFSD</th>\n",
       "      <th>WLYE</th>\n",
       "      <th>YLBH</th>\n",
       "      <th>Margalef</th>\n",
       "      <th>SWI_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>10.913462</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.87598</td>\n",
       "      <td>0.351927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>6.427586</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.10716</td>\n",
       "      <td>0.490434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>6.271242</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.15943</td>\n",
       "      <td>0.461377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.687500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57169</td>\n",
       "      <td>0.0865042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT    ...           RBTT  RDSN  RTCB  SDBS      SPDC  TFSD  WLYE  \\\n",
       "0  0.048077    ...      10.913462     0     0     0  0.019231     0     0   \n",
       "1  0.013793    ...       6.427586     0     0     0  0.013793     0     0   \n",
       "2  0.019608    ...       6.271242     0     0     0  0.016340     0     0   \n",
       "3  0.000000    ...       5.687500     0     0     0  0.000000     0     0   \n",
       "4  0.000000    ...      11.500000     0     0     0  0.000000     0     0   \n",
       "\n",
       "   YLBH  Margalef      SWI_2  \n",
       "0     0   2.87598   0.351927  \n",
       "1     0   3.10716   0.490434  \n",
       "2     0   3.15943   0.461377  \n",
       "3     0   0.57169  0.0865042  \n",
       "4     0         0          0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  define color list\n",
    "colors = ['b', 'g', 'r', 'k', 'c', 'm', 'y']\n",
    "##  define index for iterating through color list\n",
    "index = 0\n",
    "##  for each river segment\n",
    "for segment in segments:\n",
    "    ##  subset df to non-zero values for the current river segment\n",
    "    segDF = data.loc[data['RiverSeg'] == segment]\n",
    "    segDF = segDF[segDF[\"SWI_2\"]>0]\n",
    "\n",
    "    ## sort based on year\n",
    "    segDF = segDF.sort_values('Year')\n",
    "    ## define x,y for plotting\n",
    "    x = segDF[\"Year\"]\n",
    "    y = segDF[\"SWI_2\"]\n",
    "    ## change name of y to Riv Seg for legend\n",
    "    y.name = segment\n",
    "    ##  build graph...\n",
    "    ##  plot segment x vs y assigning color based on index\n",
    "    plt.plot(x,y,colors[index])\n",
    "    ##  locate legend\n",
    "    plt.legend(loc=(1.05,0.2))\n",
    "    index += 1\n",
    "\n",
    "##  update title\n",
    "plt.title(\"S-W Index (Log(2)), \" + river + \" River Segments\")\n",
    "##  label x axis\n",
    "plt.xlabel('Year')\n",
    "##  label y axis\n",
    "plt.ylabel('SWI Log(2)')\n",
    "##  force x axis to integer values, increment by 1 year\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "##  rotate year labels 90 degrees\n",
    "plt.xticks(rotation=90)\n",
    "##  advance color index    \n",
    "    \n",
    "##  save figure\n",
    "plt.savefig(output + \"\\\\\" + river + \"_SWI_2.png\", bbox_inches='tight',dpi=300, size=(2000,2000))\n",
    "##  display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## add new column\n",
    "data[\"SWI_e\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  for each row calculate Shannon-Wiener Log(e) Index\n",
    "for i in range(len(data)):\n",
    "    swi = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            swi += -(data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])) * math.log((data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])))\n",
    "    data.loc[i,'SWI_e'] = swi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>RDSN</th>\n",
       "      <th>RTCB</th>\n",
       "      <th>SDBS</th>\n",
       "      <th>SPDC</th>\n",
       "      <th>TFSD</th>\n",
       "      <th>WLYE</th>\n",
       "      <th>YLBH</th>\n",
       "      <th>Margalef</th>\n",
       "      <th>SWI_2</th>\n",
       "      <th>SWI_e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.87598</td>\n",
       "      <td>0.351927</td>\n",
       "      <td>0.243937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.10716</td>\n",
       "      <td>0.490434</td>\n",
       "      <td>0.339943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.15943</td>\n",
       "      <td>0.461377</td>\n",
       "      <td>0.319802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57169</td>\n",
       "      <td>0.0865042</td>\n",
       "      <td>0.0599602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT    ...      RDSN  RTCB  SDBS      SPDC  TFSD  WLYE  YLBH  \\\n",
       "0  0.048077    ...         0     0     0  0.019231     0     0     0   \n",
       "1  0.013793    ...         0     0     0  0.013793     0     0     0   \n",
       "2  0.019608    ...         0     0     0  0.016340     0     0     0   \n",
       "3  0.000000    ...         0     0     0  0.000000     0     0     0   \n",
       "4  0.000000    ...         0     0     0  0.000000     0     0     0   \n",
       "\n",
       "   Margalef      SWI_2      SWI_e  \n",
       "0   2.87598   0.351927   0.243937  \n",
       "1   3.10716   0.490434   0.339943  \n",
       "2   3.15943   0.461377   0.319802  \n",
       "3   0.57169  0.0865042  0.0599602  \n",
       "4         0          0          0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  define color list\n",
    "colors = ['b', 'g', 'r', 'k', 'c', 'm', 'y']\n",
    "##  define index for iterating through color list\n",
    "index = 0\n",
    "##  for each river segment\n",
    "for segment in segments:\n",
    "    ##  subset df to non-zero values for the current river segment\n",
    "    segDF = data.loc[data['RiverSeg'] == segment]\n",
    "    segDF = segDF[segDF[\"SWI_e\"]>0]\n",
    "\n",
    "    ## sort based on year\n",
    "    segDF = segDF.sort_values('Year')\n",
    "    ## define x,y for plotting\n",
    "    x = segDF[\"Year\"]\n",
    "    y = segDF[\"SWI_e\"]\n",
    "    ## change name of y to Riv Seg for legend\n",
    "    y.name = segment\n",
    "    ##  build graph...\n",
    "    ##  plot segment x vs y assigning color based on index\n",
    "    plt.plot(x,y,colors[index])\n",
    "    ##  locate legend\n",
    "    plt.legend(loc=(1.05,0.2))\n",
    "    ##  advance color index\n",
    "    index += 1\n",
    "\n",
    "##  update title\n",
    "plt.title(\"S-W Index (Log(e)), \" + river + \" River Segments\")\n",
    "##  label x axis\n",
    "plt.xlabel('Year')\n",
    "##  label y axis\n",
    "plt.ylabel('SWI Log(e)')\n",
    "##  force x axis to integer values, increment by 1 year\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "##  rotate year labels 90 degrees\n",
    "plt.xticks(rotation=90)    \n",
    "    \n",
    "    \n",
    "##  save figure\n",
    "plt.savefig(output + \"\\\\\" + river + \"_SWI_e.png\", bbox_inches='tight',dpi=300, size=(2000,2000))\n",
    "##  display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## add new column\n",
    "data[\"SWI_10\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  for each row calculate Shannon-Wiener Log(10) Index\n",
    "for i in range(len(data)):\n",
    "    swi = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            swi += -(data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])) * math.log((data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])),10)\n",
    "    data.loc[i,'SWI_10'] = swi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>RTCB</th>\n",
       "      <th>SDBS</th>\n",
       "      <th>SPDC</th>\n",
       "      <th>TFSD</th>\n",
       "      <th>WLYE</th>\n",
       "      <th>YLBH</th>\n",
       "      <th>Margalef</th>\n",
       "      <th>SWI_2</th>\n",
       "      <th>SWI_e</th>\n",
       "      <th>SWI_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.87598</td>\n",
       "      <td>0.351927</td>\n",
       "      <td>0.243937</td>\n",
       "      <td>0.10594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.10716</td>\n",
       "      <td>0.490434</td>\n",
       "      <td>0.339943</td>\n",
       "      <td>0.147635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.15943</td>\n",
       "      <td>0.461377</td>\n",
       "      <td>0.319802</td>\n",
       "      <td>0.138888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57169</td>\n",
       "      <td>0.0865042</td>\n",
       "      <td>0.0599602</td>\n",
       "      <td>0.0260404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT    ...      RTCB  SDBS      SPDC  TFSD  WLYE  YLBH  Margalef  \\\n",
       "0  0.048077    ...         0     0  0.019231     0     0     0   2.87598   \n",
       "1  0.013793    ...         0     0  0.013793     0     0     0   3.10716   \n",
       "2  0.019608    ...         0     0  0.016340     0     0     0   3.15943   \n",
       "3  0.000000    ...         0     0  0.000000     0     0     0   0.57169   \n",
       "4  0.000000    ...         0     0  0.000000     0     0     0         0   \n",
       "\n",
       "       SWI_2      SWI_e     SWI_10  \n",
       "0   0.351927   0.243937    0.10594  \n",
       "1   0.490434   0.339943   0.147635  \n",
       "2   0.461377   0.319802   0.138888  \n",
       "3  0.0865042  0.0599602  0.0260404  \n",
       "4          0          0          0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  define color list\n",
    "colors = ['b', 'g', 'r', 'k', 'c', 'm', 'y']\n",
    "##  define index for iterating through color list\n",
    "index = 0\n",
    "##  for each river segment\n",
    "for segment in segments:\n",
    "    ##  subset df to non-zero values for the current river segment\n",
    "    segDF = data.loc[data['RiverSeg'] == segment]\n",
    "    segDF = segDF[segDF[\"SWI_10\"]>0]\n",
    "\n",
    "    ## sort based on year\n",
    "    segDF = segDF.sort_values('Year')\n",
    "    ## define x,y for plotting\n",
    "    x = segDF[\"Year\"]\n",
    "    y = segDF[\"SWI_10\"]\n",
    "    ## change name of y to Riv Seg for legend\n",
    "    y.name = segment\n",
    "    ##  build graph...\n",
    "    ##  plot segment x vs y assigning color based on index\n",
    "    plt.plot(x,y,colors[index])\n",
    "    ##  locate legend\n",
    "    plt.legend(loc=(1.05,0.2))\n",
    "    ##  advance color index\n",
    "    index += 1\n",
    "\n",
    "##  update title\n",
    "plt.title(\"S-W Index (Log(10)), \" + river + \" River Segments\")\n",
    "##  label x axis\n",
    "plt.xlabel('Year')\n",
    "##  label y axis\n",
    "plt.ylabel('SWI Log(10)')\n",
    "##  force x axis to integer values, increment by 1 year\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "##  rotate year labels 90 degrees\n",
    "plt.xticks(rotation=90)    \n",
    "    \n",
    "    \n",
    "##  save figure\n",
    "plt.savefig(output + \"\\\\\" + river + \"_SWI_10.png\", bbox_inches='tight',dpi=300, size=(2000,2000))\n",
    "##  display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "## add new column\n",
    "data[\"lam\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  for each row calculate Lambda\n",
    "for i in range(len(data)):\n",
    "    N = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            N += data.iloc[i][x]\n",
    "\n",
    "    lam = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        lam += (data.iloc[i][x]/N) * (data.iloc[i][x]/N)\n",
    "    data.loc[i,'lam'] = lam\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>SDBS</th>\n",
       "      <th>SPDC</th>\n",
       "      <th>TFSD</th>\n",
       "      <th>WLYE</th>\n",
       "      <th>YLBH</th>\n",
       "      <th>Margalef</th>\n",
       "      <th>SWI_2</th>\n",
       "      <th>SWI_e</th>\n",
       "      <th>SWI_10</th>\n",
       "      <th>lam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.87598</td>\n",
       "      <td>0.351927</td>\n",
       "      <td>0.243937</td>\n",
       "      <td>0.10594</td>\n",
       "      <td>0.916313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.10716</td>\n",
       "      <td>0.490434</td>\n",
       "      <td>0.339943</td>\n",
       "      <td>0.147635</td>\n",
       "      <td>0.870082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.15943</td>\n",
       "      <td>0.461377</td>\n",
       "      <td>0.319802</td>\n",
       "      <td>0.138888</td>\n",
       "      <td>0.88243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57169</td>\n",
       "      <td>0.0865042</td>\n",
       "      <td>0.0599602</td>\n",
       "      <td>0.0260404</td>\n",
       "      <td>0.978497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT    ...     SDBS      SPDC  TFSD  WLYE  YLBH  Margalef      SWI_2  \\\n",
       "0  0.048077    ...        0  0.019231     0     0     0   2.87598   0.351927   \n",
       "1  0.013793    ...        0  0.013793     0     0     0   3.10716   0.490434   \n",
       "2  0.019608    ...        0  0.016340     0     0     0   3.15943   0.461377   \n",
       "3  0.000000    ...        0  0.000000     0     0     0   0.57169  0.0865042   \n",
       "4  0.000000    ...        0  0.000000     0     0     0         0          0   \n",
       "\n",
       "       SWI_e     SWI_10       lam  \n",
       "0   0.243937    0.10594  0.916313  \n",
       "1   0.339943   0.147635  0.870082  \n",
       "2   0.319802   0.138888   0.88243  \n",
       "3  0.0599602  0.0260404  0.978497  \n",
       "4          0          0         1  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  define color list\n",
    "colors = ['b', 'g', 'r', 'k', 'c', 'm', 'y']\n",
    "##  define index for iterating through color list\n",
    "index = 0\n",
    "##  for each river segment\n",
    "for segment in segments:\n",
    "    ##  subset df to non-zero values for the current river segment\n",
    "    segDF = data.loc[data['RiverSeg'] == segment]\n",
    "    segDF = segDF[segDF[\"lam\"]>0]\n",
    "\n",
    "    ## sort based on year\n",
    "    segDF = segDF.sort_values('Year')\n",
    "    ## define x,y for plotting\n",
    "    x = segDF[\"Year\"]\n",
    "    y = segDF[\"lam\"]\n",
    "    ## change name of y to Riv Seg for legend\n",
    "    y.name = segment\n",
    "    ##  build graph...\n",
    "    ##  plot segment x vs y\n",
    "    plt.plot(x,y,colors[index])\n",
    "    ##  locate legend\n",
    "    plt.legend(loc=(1.05,0.2))\n",
    "    ##  advance color index\n",
    "    index += 1\n",
    "\n",
    "##  update title\n",
    "plt.title(\"Lambda, \" + river + \" River Segments\")\n",
    "##  label x axis\n",
    "plt.xlabel('Year')\n",
    "##  label y axis\n",
    "plt.ylabel('Lambda')\n",
    "##  force x axis to integer values, increment by 1 year\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "##  rotate year labels 90 degrees\n",
    "plt.xticks(rotation=90)    \n",
    "    \n",
    "    \n",
    "##  save figure\n",
    "plt.savefig(output + \"\\\\\" + river + \"_Lambda.png\", bbox_inches='tight',dpi=300, size=(2000,2000))\n",
    "##  display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "## add new column\n",
    "data[\"1-lam\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  for each row calculate 1-Lambda\n",
    "for i in range(len(data)):\n",
    "    N = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            N += data.iloc[i][x]\n",
    "\n",
    "    lam = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        lam += (data.iloc[i][x]/N) * (data.iloc[i][x]/N)\n",
    "    data.loc[i,'1-lam'] = 1 - lam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>SPDC</th>\n",
       "      <th>TFSD</th>\n",
       "      <th>WLYE</th>\n",
       "      <th>YLBH</th>\n",
       "      <th>Margalef</th>\n",
       "      <th>SWI_2</th>\n",
       "      <th>SWI_e</th>\n",
       "      <th>SWI_10</th>\n",
       "      <th>lam</th>\n",
       "      <th>1-lam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.87598</td>\n",
       "      <td>0.351927</td>\n",
       "      <td>0.243937</td>\n",
       "      <td>0.10594</td>\n",
       "      <td>0.916313</td>\n",
       "      <td>0.0836871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.10716</td>\n",
       "      <td>0.490434</td>\n",
       "      <td>0.339943</td>\n",
       "      <td>0.147635</td>\n",
       "      <td>0.870082</td>\n",
       "      <td>0.129918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.15943</td>\n",
       "      <td>0.461377</td>\n",
       "      <td>0.319802</td>\n",
       "      <td>0.138888</td>\n",
       "      <td>0.88243</td>\n",
       "      <td>0.11757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57169</td>\n",
       "      <td>0.0865042</td>\n",
       "      <td>0.0599602</td>\n",
       "      <td>0.0260404</td>\n",
       "      <td>0.978497</td>\n",
       "      <td>0.0215028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT    ...          SPDC  TFSD  WLYE  YLBH  Margalef      SWI_2  \\\n",
       "0  0.048077    ...      0.019231     0     0     0   2.87598   0.351927   \n",
       "1  0.013793    ...      0.013793     0     0     0   3.10716   0.490434   \n",
       "2  0.019608    ...      0.016340     0     0     0   3.15943   0.461377   \n",
       "3  0.000000    ...      0.000000     0     0     0   0.57169  0.0865042   \n",
       "4  0.000000    ...      0.000000     0     0     0         0          0   \n",
       "\n",
       "       SWI_e     SWI_10       lam      1-lam  \n",
       "0   0.243937    0.10594  0.916313  0.0836871  \n",
       "1   0.339943   0.147635  0.870082   0.129918  \n",
       "2   0.319802   0.138888   0.88243    0.11757  \n",
       "3  0.0599602  0.0260404  0.978497  0.0215028  \n",
       "4          0          0         1          0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  define color list\n",
    "colors = ['b', 'g', 'r', 'k', 'c', 'm', 'y']\n",
    "##  define index for iterating through color list\n",
    "index = 0\n",
    "##  for each river segment\n",
    "for segment in segments:\n",
    "    ##  subset df to non-zero values for the current river segment\n",
    "    segDF = data.loc[data['RiverSeg'] == segment]\n",
    "    segDF = segDF[segDF[\"1-lam\"]>0]\n",
    "\n",
    "    ## sort based on year\n",
    "    segDF = segDF.sort_values('Year')\n",
    "    ## define x,y for plotting\n",
    "    x = segDF[\"Year\"]\n",
    "    y = segDF[\"1-lam\"]\n",
    "    ## change name of y to Riv Seg for legend\n",
    "    y.name = segment\n",
    "    ##  build graph...\n",
    "    ##  plot segment x vs y\n",
    "    plt.plot(x,y,colors[index])\n",
    "    ##  locate legend\n",
    "    plt.legend(loc=(1.05,0.2))\n",
    "    ##  advance color index\n",
    "    index += 1\n",
    "\n",
    "##  update title\n",
    "plt.title(\"1-Lambda, \" + river + \" River Segments\")\n",
    "##  label x axis\n",
    "plt.xlabel('Year')\n",
    "##  label y axis\n",
    "plt.ylabel('1-Lambda')\n",
    "##  force x axis to integer values, increment by 1 year\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "##  rotate year labels 90 degrees\n",
    "plt.xticks(rotation=90)    \n",
    "    \n",
    "##  save figure   \n",
    "plt.savefig(output + \"\\\\\" + river + \"_1-Lambda.png\", bbox_inches='tight',dpi=300, size=(2000,2000))\n",
    "##  show figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "## add new column\n",
    "data[\"lam'\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  for each row calculate Lambda Prime\n",
    "for i in range(len(data)):\n",
    "    N = 0.0\n",
    "    for x in range(len(data.iloc[i]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            N += data.iloc[i][x]\n",
    "    \n",
    "    \n",
    "    array = data.iloc[i][4:last]\n",
    "    num = 0.0\n",
    "    for y in array:\n",
    "        num += (y * (y-1))\n",
    "    lam = num/(N*(N-1))\n",
    "    data.loc[i,\"lam'\"] = lam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>TFSD</th>\n",
       "      <th>WLYE</th>\n",
       "      <th>YLBH</th>\n",
       "      <th>Margalef</th>\n",
       "      <th>SWI_2</th>\n",
       "      <th>SWI_e</th>\n",
       "      <th>SWI_10</th>\n",
       "      <th>lam</th>\n",
       "      <th>1-lam</th>\n",
       "      <th>lam'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.87598</td>\n",
       "      <td>0.351927</td>\n",
       "      <td>0.243937</td>\n",
       "      <td>0.10594</td>\n",
       "      <td>0.916313</td>\n",
       "      <td>0.0836871</td>\n",
       "      <td>0.908269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.10716</td>\n",
       "      <td>0.490434</td>\n",
       "      <td>0.339943</td>\n",
       "      <td>0.147635</td>\n",
       "      <td>0.870082</td>\n",
       "      <td>0.129918</td>\n",
       "      <td>0.848049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.15943</td>\n",
       "      <td>0.461377</td>\n",
       "      <td>0.319802</td>\n",
       "      <td>0.138888</td>\n",
       "      <td>0.88243</td>\n",
       "      <td>0.11757</td>\n",
       "      <td>0.86173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57169</td>\n",
       "      <td>0.0865042</td>\n",
       "      <td>0.0599602</td>\n",
       "      <td>0.0260404</td>\n",
       "      <td>0.978497</td>\n",
       "      <td>0.0215028</td>\n",
       "      <td>0.97397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT    ...     TFSD  WLYE  YLBH  Margalef      SWI_2      SWI_e  \\\n",
       "0  0.048077    ...        0     0     0   2.87598   0.351927   0.243937   \n",
       "1  0.013793    ...        0     0     0   3.10716   0.490434   0.339943   \n",
       "2  0.019608    ...        0     0     0   3.15943   0.461377   0.319802   \n",
       "3  0.000000    ...        0     0     0   0.57169  0.0865042  0.0599602   \n",
       "4  0.000000    ...        0     0     0         0          0          0   \n",
       "\n",
       "      SWI_10       lam      1-lam      lam'  \n",
       "0    0.10594  0.916313  0.0836871  0.908269  \n",
       "1   0.147635  0.870082   0.129918  0.848049  \n",
       "2   0.138888   0.88243    0.11757   0.86173  \n",
       "3  0.0260404  0.978497  0.0215028   0.97397  \n",
       "4          0         1          0         1  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  define color list\n",
    "colors = ['b', 'g', 'r', 'k', 'c', 'm', 'y']\n",
    "##  define index for iterating through color list\n",
    "index = 0\n",
    "##  for each river segment\n",
    "for segment in segments:\n",
    "    ##  subset df to non-zero values for the current river segment\n",
    "    segDF = data.loc[data['RiverSeg'] == segment]\n",
    "    segDF = segDF[segDF[\"lam'\"]>0]\n",
    "\n",
    "    ## sort based on year\n",
    "    segDF = segDF.sort_values('Year')\n",
    "    ## define x,y for plotting\n",
    "    x = segDF[\"Year\"]\n",
    "    y = segDF[\"lam'\"]\n",
    "    ## change name of y to Riv Seg for legend\n",
    "    y.name = segment\n",
    "    ##  build graph...\n",
    "    ##  plot segment x vs y\n",
    "    plt.plot(x,y,colors[index])\n",
    "    ##  locate legend\n",
    "    plt.legend(loc=(1.05,0.2))\n",
    "    ##  advance color index\n",
    "    index += 1\n",
    "\n",
    "##  update title\n",
    "plt.title(\"Lambda', \" + river + \" River Segments\")\n",
    "##  label x axis\n",
    "plt.xlabel('Year')\n",
    "##  label y axis\n",
    "plt.ylabel(\"Lambda'\")\n",
    "##  force x axis to integer values, increment by 1 year\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "##  rotate year labels 90 degrees\n",
    "plt.xticks(rotation=90)    \n",
    "    \n",
    "##  save figure\n",
    "plt.savefig(output + \"\\\\\" + river + \"_Lambda'.png\", bbox_inches='tight',dpi=300, size=(2000,2000))\n",
    "##  show figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  add new column\n",
    "data[\"1-lam'\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  for each row calculate 1-Lambda Prime\n",
    "for i in range(len(data)):\n",
    "    N = 0.0\n",
    "    for x in range(len(data.iloc[i]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            N += data.iloc[i][x]\n",
    "    \n",
    "    \n",
    "    array = data.iloc[i][4:last]\n",
    "    num = 0.0\n",
    "    for y in array:\n",
    "        num += (y * (y-1))\n",
    "    lam = num/(N*(N-1))\n",
    "    data.loc[i,\"1-lam'\"] = 1 - lam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>WLYE</th>\n",
       "      <th>YLBH</th>\n",
       "      <th>Margalef</th>\n",
       "      <th>SWI_2</th>\n",
       "      <th>SWI_e</th>\n",
       "      <th>SWI_10</th>\n",
       "      <th>lam</th>\n",
       "      <th>1-lam</th>\n",
       "      <th>lam'</th>\n",
       "      <th>1-lam'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.87598</td>\n",
       "      <td>0.351927</td>\n",
       "      <td>0.243937</td>\n",
       "      <td>0.10594</td>\n",
       "      <td>0.916313</td>\n",
       "      <td>0.0836871</td>\n",
       "      <td>0.908269</td>\n",
       "      <td>0.091731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.10716</td>\n",
       "      <td>0.490434</td>\n",
       "      <td>0.339943</td>\n",
       "      <td>0.147635</td>\n",
       "      <td>0.870082</td>\n",
       "      <td>0.129918</td>\n",
       "      <td>0.848049</td>\n",
       "      <td>0.151951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.15943</td>\n",
       "      <td>0.461377</td>\n",
       "      <td>0.319802</td>\n",
       "      <td>0.138888</td>\n",
       "      <td>0.88243</td>\n",
       "      <td>0.11757</td>\n",
       "      <td>0.86173</td>\n",
       "      <td>0.13827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57169</td>\n",
       "      <td>0.0865042</td>\n",
       "      <td>0.0599602</td>\n",
       "      <td>0.0260404</td>\n",
       "      <td>0.978497</td>\n",
       "      <td>0.0215028</td>\n",
       "      <td>0.97397</td>\n",
       "      <td>0.0260297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT    ...      WLYE  YLBH  Margalef      SWI_2      SWI_e     SWI_10  \\\n",
       "0  0.048077    ...         0     0   2.87598   0.351927   0.243937    0.10594   \n",
       "1  0.013793    ...         0     0   3.10716   0.490434   0.339943   0.147635   \n",
       "2  0.019608    ...         0     0   3.15943   0.461377   0.319802   0.138888   \n",
       "3  0.000000    ...         0     0   0.57169  0.0865042  0.0599602  0.0260404   \n",
       "4  0.000000    ...         0     0         0          0          0          0   \n",
       "\n",
       "        lam      1-lam      lam'     1-lam'  \n",
       "0  0.916313  0.0836871  0.908269   0.091731  \n",
       "1  0.870082   0.129918  0.848049   0.151951  \n",
       "2   0.88243    0.11757   0.86173    0.13827  \n",
       "3  0.978497  0.0215028   0.97397  0.0260297  \n",
       "4         1          0         1          0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  define color list\n",
    "colors = ['b', 'g', 'r', 'k', 'c', 'm', 'y']\n",
    "##  define index for iterating through color list\n",
    "index = 0\n",
    "##  for each river segment\n",
    "for segment in segments:\n",
    "    ##  subset df for the current river segment\n",
    "    segDF = data.loc[data['RiverSeg'] == segment]\n",
    "    segDF = segDF[segDF[\"lam'\"]>0]\n",
    "    ## sort based on year\n",
    "    segDF = segDF.sort_values('Year')\n",
    "    ## define x,y for plotting\n",
    "    x = segDF[\"Year\"]\n",
    "    y = segDF[\"1-lam'\"]\n",
    "    ## change name of y to Riv Seg for legend\n",
    "    y.name = segment\n",
    "    ##  build graph...\n",
    "    ##  plot segment x vs y\n",
    "    plt.plot(x,y,colors[index])\n",
    "    ##  locate legend\n",
    "    plt.legend(loc=(1.05,0.2))\n",
    "    ##  advance color index\n",
    "    index += 1\n",
    "\n",
    "##  update title\n",
    "plt.title(\"1-Lambda', \" + river + \" River Segments\")\n",
    "##  label x axis\n",
    "plt.xlabel('Year')\n",
    "##  label y axis\n",
    "plt.ylabel(\"1-Lambda'\")\n",
    "##  force x axis to integer values, increment by 1 year\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "##  rotate year labels 90 degrees\n",
    "plt.xticks(rotation=90)    \n",
    "    \n",
    "    \n",
    "##  save figure\n",
    "plt.savefig(output + \"\\\\\" + river + \"_1-Lambda'.png\", bbox_inches='tight',dpi=300, size=(2000,2000))\n",
    "##  display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"N1\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  for each row calculate Hill N1\n",
    "for i in range(len(data)):\n",
    "    N1 = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            N1 += -(data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])) * math.log((data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])))\n",
    "    data.loc[i,'N1'] = math.exp(N1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>YLBH</th>\n",
       "      <th>Margalef</th>\n",
       "      <th>SWI_2</th>\n",
       "      <th>SWI_e</th>\n",
       "      <th>SWI_10</th>\n",
       "      <th>lam</th>\n",
       "      <th>1-lam</th>\n",
       "      <th>lam'</th>\n",
       "      <th>1-lam'</th>\n",
       "      <th>N1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2.87598</td>\n",
       "      <td>0.351927</td>\n",
       "      <td>0.243937</td>\n",
       "      <td>0.10594</td>\n",
       "      <td>0.916313</td>\n",
       "      <td>0.0836871</td>\n",
       "      <td>0.908269</td>\n",
       "      <td>0.091731</td>\n",
       "      <td>1.27626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3.10716</td>\n",
       "      <td>0.490434</td>\n",
       "      <td>0.339943</td>\n",
       "      <td>0.147635</td>\n",
       "      <td>0.870082</td>\n",
       "      <td>0.129918</td>\n",
       "      <td>0.848049</td>\n",
       "      <td>0.151951</td>\n",
       "      <td>1.40487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3.15943</td>\n",
       "      <td>0.461377</td>\n",
       "      <td>0.319802</td>\n",
       "      <td>0.138888</td>\n",
       "      <td>0.88243</td>\n",
       "      <td>0.11757</td>\n",
       "      <td>0.86173</td>\n",
       "      <td>0.13827</td>\n",
       "      <td>1.37686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57169</td>\n",
       "      <td>0.0865042</td>\n",
       "      <td>0.0599602</td>\n",
       "      <td>0.0260404</td>\n",
       "      <td>0.978497</td>\n",
       "      <td>0.0215028</td>\n",
       "      <td>0.97397</td>\n",
       "      <td>0.0260297</td>\n",
       "      <td>1.06179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT   ...     YLBH  Margalef      SWI_2      SWI_e     SWI_10  \\\n",
       "0  0.048077   ...        0   2.87598   0.351927   0.243937    0.10594   \n",
       "1  0.013793   ...        0   3.10716   0.490434   0.339943   0.147635   \n",
       "2  0.019608   ...        0   3.15943   0.461377   0.319802   0.138888   \n",
       "3  0.000000   ...        0   0.57169  0.0865042  0.0599602  0.0260404   \n",
       "4  0.000000   ...        0         0          0          0          0   \n",
       "\n",
       "        lam      1-lam      lam'     1-lam'       N1  \n",
       "0  0.916313  0.0836871  0.908269   0.091731  1.27626  \n",
       "1  0.870082   0.129918  0.848049   0.151951  1.40487  \n",
       "2   0.88243    0.11757   0.86173    0.13827  1.37686  \n",
       "3  0.978497  0.0215028   0.97397  0.0260297  1.06179  \n",
       "4         1          0         1          0        1  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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21StV5+nOT7Nk1xJWpawqBYWGsogxsDZYA5wc4R62UqtKLW6LvI1ZG2eZ2rEGg4v4ZOMn7Dy2kyk9p+Dt5V2oa0Z3GE1IlRBe+O0FkzTGUCyMgbVh/8n9pJ9Od6iBBR3slHk+ky8Sv3Bov57G9iPbeWP1G66WYShnnM4+zcTlE+lSvwv9mvYr9HVVKlThuaufY/me5SxLXuZEhYayijGwNsSnxgOOCXCy5aq6VxFTO4Z31r9Trp+E/7v6vzzx8xOkZaW5WoqhHDFt7TQOZh1kau+piEiRrr0/5n7qVatnRrGGYuF0Aysi3iKyQUQuWasiIsNEJF1ENlq2Ec7WczniU+Px8fKhTUgbh/YrIozuMJqt6VtZvme5Q/v2JOL2xQGw4eAGFysxlBeOnznO1FVT6dukL10bdC3y9ZV8KjH+mvGs2b+GH3b+4ASFhrJMaYxgHwW2Xeb8PKVUlGX7qBT0FEj8wXha1WpFJZ9KDu97UOQgalSuwbvr33V4357AsTPH2Jq+FYCNaRtdrMZQXnh11aucOHuCKb2mFLuPYVHDiAiM4IXfXjClKA1FwqkGVkTqAX0BlxrOwqCUIiE1wSEJJuxR2bcyI6JH8O32b9l/cr9T7uHO/LHvD0Cnkdx4yBhYg/M5cPIAb619izva3FEir5Svty8Tu01kY9pGvt72tQMVGso6zh7Bvgk8DVzuse9mEUkUkQUiUt/Jegpk74m9HD1z1OHzr7aMaj+KPJXHh/EfOu0e7kpcShy+Xr78u9G/zQjWUCpMWjGJ3LxcJnWfVOK+hrQeQougFoz/bbxJfWooNE4zsCLSDzislLpcKpTvgTClVBvgV+DTAvq6X0TiRSQ+Pd051WmcFeBkS1hAGP2a9mP6n9M5l3POafdxR+JS4oipE0Onep3YeXQnWeezXC3JUIb56+hffLzhY0bGjiQ8MLzE/Xl7eTOpxyS2HdnGnKQ5DlBoKA84cwTbBegvInuAL4GeInLROhWl1FGllNXSzADsZthXSk1XSsUqpWKDg4OdIjY+NR5fL19a1WrllP6tjO4wmsOnDrNg6wKn3sedOJtzlvWp6+lavytRoVEoFEmHklwty1CGGffbOCr5VOL5q593WJ8DWwwkKjSKiSsmkp2b7bB+DWUXpxlYpdRYpVQ9pVQYMBhYppS607aNiNS2edmfywdDOZX41HjahLS5Ygq1ktI7ojdNajQpV8FOCakJnM89T9cGXS/U2DVuYoOzSEhN4H9b/sfjnR4nxD/EYf16iReTe0xm9/HdzNo4y2H9Gsoupb4OVkQmiUh/y8tHRGSLiGwCHgGGlbYesAQ4HUxwqnvYipd48VD7h1i9f3W5SSQel6KX53Su35n61epTo3INY2ANTmPs0rHUrFyTJzs/6fC++zbpy1V1r2LSykmczTnr8P4NZYtSMbBKqeVKqX6W/fFKqYWW/bFKqUilVFulVA+llEsy4u8+vpuMsxmlYmAB7o66myq+VcrNKDZuXxzNajYjuEowIkJUaBQb0sxaWIPjWbp7Kb/s/oXnr36eahWrObx/EeHlni+z/+R+pidMd3j/hrKFyeTEPwFOMbVLVmS9sARUCuDONncyd/Ncjp4+Wir3dBV5Ko9VKasuWuQfFRJF0uEkcvJyXKjMUNbIycvh2aXP0qB6A0a1H+W0+/QM70n3sO5M+X0Kp86fctp9DJ6Pj6sFuAPxqfFU9K5IZK3IUrvnQ+0f4sOED5m5YSZPdXmq1O5b2mw/sp3jZ49fbGBDozibc5a/jv5Fy+CWLlRn8BROnjvJgZMHOJB54NKflv1Dpw6Rp/L45MZPnJIsxoqI8FKPl+j6SVfeXf8uT3d52mn3Mng2xsCii6y3DW1LBe8KpXbP1iGtuabhNbwf/z6Pd3q80BU+PA3r/GuX+l0uHLMNdDIGtnyTk5dDWlZagUYzNTOVA5kH7C7rCqwUSN1qdalbtS5tarWhbrW6RAZHcmvkrU7X3aVBF/o07sPklZPp17Sf+T822KXcG9g8lUfCwQTubH3nlRs7mNHtR3PbgttYsmtJkap8eBJxKXHUqlKLxjUaXzjWPKg5FbwrsDFtI0NaD3GhOoOzUErpUWcBI87UzNSLRp22+Hr5UqdqHepWq0ubkDb0adznwuu6VetSt1pd6lSt47CazcVl+g3TiZkew4AvB7D+vvVUr1TdpXoM7ke5N7C7ju3i5LmTxNQpnflXWwY0H0CdqnV4Z907ZdrAdm3Q9aIqJr7eer2xiST2TLJzs/WoM5/xTM1Kvej1qexL5ydrVK5B3araQFpHnVajaf0Z5BeEl7h/eEi9avVYcOsCen7Wkzu/uZPvBn/nEboNpUe5N7ClkcGpIHy9fXkg5gEmLJ/AzqM7aVKzSalrcCapmakkZyTzcIeHLzkXHRrNwh0LUUoVuYSYoXRRSjF55WQW7ljIgcwDHMo6hOLi0m22o862IW25vvH1lxjPOlXrUNm3sovehXO4uuHVvHntm4xeMpqJyycyqUfJ0zIayg7l3sAmpCZQyaeSy+ZQ7o+5n5dWvsR769/jv9f91yUanMWqlFWAnq/KT1RoFB9v+JiDWQepU7VOaUszFIHXVr3GhOUT6NqgK32b9L1kxFm3al1q+tUst6O3B9s/SMLBBCavnEx0aDQ3tbjJ1ZIMbkK5N7DxB+OJDo3Gx8s1v4pQ/1BuaXkLn2z8hJd6vkSVClVcosMZxKXEUdmnMtGh0ZecswY6bTi4wRhYN2b+lvk8u/RZbm91O7MHzjbeBjuICO/1fY/Nhzdz17d3sS5oHS2CW7halsENKJ+PnBZy83L58+Cfpbb+tSAeav8QJ86dYHbSbJfqcDSr9q2iY72O+Hr7XnLOWj7MzMO6L6v3rWboN0PpUr8LM2+caYzrZajkU4mvB32Nn68fA+YN4MTZE66WZHADyrWB/evoX2Sdz3LJ/Kstnet3Jio0infWvYNS6soXeACZ5zLZkLbhovWvtlSrWI1GgY1MbVg3Zffx3dz45Y3Ur16fbwd/69R1pWUFa9DT7uO7ufObO01xdkP5NrCuDHCyRUR4qP1DJB1OurBu1NNZe2AteSrvovWv+YkKjTIjWDfk+JnjXD/7enJVLj8M+YEgvyBXS/IYrEFPi/5axMTlE10tx+BiyrWBTTiYgJ+vH82DmrtaCkNaDyGgUgDvrH/H1VIcQlxKHF7iRaf6nQpsExUaxa5ju8g8l1mKygyX43zueQb+byDJGcl8O+jbMhfZXho82P5B7om6h8krJ/Pt9m9dLcfgQsq1gY1Pjadd7XZukUXJz9eP4dHD+Xrb16RmprpaTolZtW8VbULaXDbhujXQKfFQYmnJMlwGpRT3f38/y/csZ2b/mVzd8GpXS/JIrEFP7eu0Z+g3Q9mW7rIqnAYXU24NbE5eDhvSNrg8wMmWUbGjyM3L9fgqHTl5Oazet/qy7mHgQnSxcRO7By///jKfbvqUF7u/yB1t7nC1HI/GBD0ZoBwb2O1HtnM6+7TL519taVSjEbF1Yj1+HnZT2iZOZZ8qMMDJSp2qdQjyCzKl69yAOUlzGPfbOO5qexfjrhnnajllAhP0ZCi3BtZa7NydDCxA4xqN2X18t6tllAjrA8KVDKy1NqwZwbqW3/f+zj3f3UP3sO7MuGGGWY7jQGyDnl5c/qKr5RhKGacbWBHxFpENIrLIzrmKIjJPRHaJyFoRCXO2HivxqfH4V/Cnac2mpXXLQhERGEHKiRSPrpW6at8qGlZvSL1q9a7YNiokis2HN5Odm10Kygz52Xl0JwPmDSA8IJyvbvuqVCtKlRcebP8gw6KGMWnlJBP0VM4ojRHso0BBs/zDgeNKqcbAf4FXS0EPoDM4tavdzu3Su0UERpCrctl3Yp+rpRQLpRRxKXF20yPaIyo0inO559hxdIeTlRnyc/T0Ua6fcz1e4sXiIYupUbmGqyWVSUSE9/u+b4KeyiFOtS4iUg/oC3xUQJMbgU8t+wuAXlIK/qmcvBw2pm0ktrZ7uYcBwgPCATzWTZyckczBrIN0rX9597AV29qwhtLjXM45BswbwL4T+/hu8Hc0qtHI1ZLKNCboqXzi7OHbm8DTQEGz+3WBfQBKqRzgBFAzfyMRuV9E4kUkPj09vcSitqZv5WzOWbebfwU9ggVtqDyRws6/WmkW1IxKPpWMgS1FlFLcu/Be4lLi+Oymz+hcv7OrJZUL6lWrx/xb57P7+G7u/vbuMpO1zVAwTjOwItIPOKyUSrhcMzvHLvmvU0pNV0rFKqVig4ODS6zNXTI42aNetXr4ePl47Ah2VcoqqlesTmStyEK19/HyoXWt1sbAliITlk9gTtIcXun1CrdF3uZqOeWKaxpew2u9X+O7Hd/x7vp3XS3H4GScOYLtAvQXkT3Al0BPEfkiX5v9QH0AEfEBqgPHnKgJ0Aa2WsVqbukW8/bypmH1hh5rYOP2xdG5fucizW1bI4nNE73z+XTjp0xeOZnh0cN5psszrpZTLhnTcQx9m/TliZ+fMA+WZRynGVil1FilVD2lVBgwGFimlLozX7OFwN2W/VssbZz+LRufGk9M7Ri3C3CyEhEY4ZEu4mNnjrE1fWuh3cNWokKjOHrmKPtP7neSMgPAb8m/cd/399ErvBfv933fLMdxESLCJzd+QpBfEIMXDCbrfJarJRmcRKlbGBGZJCL9LS8/BmqKyC7gceBZZ9//fO55Eg8luqV72EpEYIRHjmD/2PcHUPj5Vysm0Mn5bEvfxsD/DaRJzSYsuG2B3RKChtIjuEowX9z0BX8d/YuHlzzsajkGJ1EqBlYptVwp1c+yP14ptdCyf1YpdatSqrFSqoNSyulWZcvhLZzLPefWBjY8IJwjp494XBL8uJQ4fL18aV+nfZGua12rNYIYA+skDp86TN85fanoXZHFQxYTUCnA1ZIMQI/wHrxwzQvM2jiL2Yllqxa0QeOePlInYg1wcqccxPnx1EjiuJQ4YurEUNm3cpGuq1qxKo1rNDa1YZ3Amewz3PjljaRlpbHw9oWEBYS5WpLBhvHdxtO1QVdGLh7JrmO7XC3H4GDKpYENqBRwwYi5I+GBnrcW9mzOWdanri/0+tf8mJSJjidP5XH3t3ezdv9aZg+cTYe6HVwtyZAPHy8fZg+cja+XL4MXDOZ87nlXSzI4kPJnYA/GE1sn1q0DPKzG35MMbEJqAudzzxd5/tVKdGg0u4/vNgvwHcjzS59n/tb5/Odf/+GmFje5Wo6hABpUb8DMG2eScDCBsb+OdbUcgwMpVwb2XM45kg4luWUGJ1sCKwVSvWJ1ko97jovYmmCiuEkLTG1YxzIjYQZTV01lZMxIHu/0uKvlGK7AgOYDeKj9Q7yx5g1+2PmDq+UYHES5MrBJh5PIzssmpo77zr+CDuMPDwxnd4bnjGDj9sXRrGYzgqsULxGIiSR2HL/8/QujFo/iusbXMe36aW7trTH8w//9+/9oE9KGu7+9m9TMVFfLMTiAcmVg3TmDU348aalOnspjVcqqYruHAUL9Q6lVpZapDVtCNh/ezC3zbyGyViTzbpmHj5ePqyUZCkkln0rMu2Uep7NPM/SboeTm5bpakqGElDsDW7NyTRpWb+hqKVckIiCCPRl7PKJI8/Yj2zl+9niJDKypDVty0rLS6DunL1V8q7Do9kVUq1jN1ZIMRaR5UHPe6fMOy5KXMTVuqqvlGEpIuTKwCQcT3D7AyUp4YDhnc86SlpXmailXxDr/2qV+4UrUFURUSBRb0reYSMpicDr7NDfMvYEjp4+waMgi6lev72pJhmIyLGoYt7e6nQnLJ7AqZZWr5RhKQLkxsGeyz7D58GaPcA+DZ0USx6XEUatKLRrXaFyifqJCozife57tR7Y7SFn5IDcvlzu+voM/D/7Jlzd/Sbva7VwtyVACRIQP+n1Aw4CGDPl6CMfPHHe1JEMxKTcGNvFQIjl5OW6dYMKWC8kmPCCSOC4ljq4NupbYMxBdOxowgU5F5elfnubb7d/y5rVvckOzG1wtx+AAqlWsxpc3f0lqZiojvh9hCmF4KOUmAiI1M5VqFat5zAi2YfWGCOL2I9jUzFSSM5J5uEPJ86k2qdGEyj6V2Zi2kbva3uUAdWWf99a/xxtr3uCRDo/w8FXlPKdtejo0Sln6AAAgAElEQVS89RacOQPe3v9sPj5Xfh0eDv/6l6vfwUW0r9ueqb2m8uQvT/JB/AeMaj/K1ZIMRaTcGNibWtzEjc1vROyWoHU/KvpUpG61um6/VMc6R9SlQcnmX0GX6msT0saMYAvJDzt/4OElD3ND0xt449o3XC3HtZw5A/37w7p1ULky5OZCTo7+WZjRn4g20DVrOl9rEXis02P8mvwrj/30GF0adKFNSBtXSzIUgXLjIgbwEi+PCHCyEhEY4fYu4riUOCr7VCY6NNoh/UWFRrEhbYNxiV2BTWmbGLRgEG1D2jLn5jl4e3m7WpLryMuDe+6BNWtg/nzIytIGNztbn8vL0/tnz8KpU3DiBBw7pg1qWhosWKCN8J9/uvqdXIKXePHpgE8JrBzI4AWDOXX+lKslGYpAuTKwnkZ4QLjbu4hX7VtFx3odHVb+LCo0ioyzGaScSHFIf2WRAycP0HdOXwIqBbBoyCL8K/i7WpJrmTAB5s2DV1+FgQMvPS+i3cAVK4KfH1SrBoGBEBQEISHQq5duFx9furoLSa0qtfjipi/YfmQ7zy19ztVyDEXAGFg3JiIwggOZBzibc9bVUuySeS6TDWkbSrT+NT8mo9PlyTqfRb+5/Thx7gSLhyymTtU6rpbkWj77DF56CYYPh6eeKl4fAQHQuDEkJDhWmwPpFdGLj/p/xJOdn3S1FEMRcJqBFZFKIrJORDaJyBYRedFOm2Eiki4iGy3bCGfp8USskcR7M/a6WIl91h5YS57KK/H6V1tMbdiCyc3LZfCCwSQdSmL+rfPNfNzKlTBiBPTsCe+9p0eqxSU21m1HsFbujb7XrG/2MJw5gj0H9FRKtQWigOtEpKOddvOUUlGW7SMn6vE4wgOcW7bu1PlT3PH1HczcMJOcvJwiXx+XEoeXeNGpfieHaapSoQrNgpqZ2rB2eOynx1i8czHT+kzjusbXuVqOa9m5E266CSIi9BxqhQol6y8mBvbuhSNHHKPPYMCJBlZpsiwvfS2biVwpAs5ONrF6/2rmJM1h+MLhtP2gLd9t/65IwUWr9q2iTUgbh6fkMykTL+WtNW8xbd00nuj0hFmucewY9O2rR6yLF+v51JISa1m+58ZuYoPn4dQ5WBHxFpGNwGHgF6XUWjvNbhaRRBFZICLG/2FDqH8olXwqkZzhnEhia2m4GTfMICcvhwHzBtD1k64XUh9ejpy8HFbvW13sAuuXIyokij0Ze8g4m+Hwvj2R77Z/x2M/PcZNzW/itX+95mo5ruX8eR3ItHcvfPstNGrkmH6jLVHwbu4mNngWTjWwSqlcpVQUUA/oICKt8jX5HghTSrUBfgU+tdePiNwvIvEiEp+enu5MyW6FiDg1kjjxUCKh/qGMaDeCLQ9u4cN+H5J8PJmrP7ma/nP7s+XwlgKv3ZS2iVPZpxyy/jU/1kCnTWmbHN63p5GQmsCQr4cQWyeWLwZ+gZeU47hEpWDkSFixAmbOhK4OfLirXh2aNjUjWINDKZVPq1IqA1gOXJfv+FGl1DnLyxmA3TyGSqnpSqlYpVRscHDx6o16Ks4sW5d4KPFCoIyPlw/3x9zPrkd2MaXnFFbsXUGbD9pw73f3su/EvkuutY5yHRlBbMVqYMt76bqUEyn0m9uPYL9gFt6+ED9fP1dLci2vvgqffKKX5dxxh+P794BAJ4Nn4cwo4mARCbDsVwZ6A9vztalt87I/sM1ZejyViMAIkjOSHZ54IScvhy3pW2hT6+JIVD9fP8ZePZbdj+xmzFVjmJ00mybTmvDUz09x7MyxC+1W7VtFw+oNqVetnkN1AYT4hxDqH1qu52FPnjtJvzn9OJ19msVDFhPqH+pqSa5lwQIYOxZuv10bWGcQEwP79sHhw87p31DucOYItjbwm4gkAuvRc7CLRGSSiPS3tHnEsoRnE/AIMMyJejyS8IBwTp47eZFxcwR/Hf2L87nnC1zqUdOvJq9f+zp/jf6Lwa0G8/rq14l4K4KpcVM5nX36QoJ/Z1GeA52yc7O5bf5tbDuyja9u+4rIWpGuluRa1q2DoUOhc2ftGnZWNjYT6GRwMM6MIk5USkUrpdoopVoppSZZjo9XSi207I9VSkUqpdoqpXoopUydsnw4K5I46VASAK1DWl+2XcOAhswaMItNIzdxdcOrGbt0LOFvhXMw66BD17/mJzo0mq3pW8tdbVilFA8veZif/v6JD/p+QO+I3q6W5Fr27tU5hmvX1kFNlSo5717R0dp4GzexwUGU44gJz+BC2ToHRxInHkrEW7xpEdSiUO1bh7Tm+9u/Z8WwFUQERuDj5UOviF4O1WRLVGgU2XnZbE3f6rR7uCOvr36dDxM+ZGzXsQxvN9zVclzLyZPQr5/OIbx4MTg7/qJqVWjWzBhYg8MoN9V0PJXwQOckm0g8nEjzoOZU9KlYpOuuaXgNf9z7B8fOHKOmn/Mqj9imTLTul3W+2voVT/3yFLdF3sZLPV9ytRzXkpMDgwbB9u3w44/QonAPgiUmNhZ++6107mUo85gR7JX48ku4+WZ4/XVdreN86bos/Sv4E+wX7HgDaxNBXFRExKnGFaBRYCOq+FYpN/Owa/ev5c5v7qRTvU7MunFW+V6Ok5MDjzyiDet77/2TjL80iImBAwd0lR2DoYSU409xIcnMhI0b4cknoVMnvV7ummvg2Wfh++/h6FGnS7BGEjsKa7Uad85lW55qwyYfT6b/l/2pU7UO3w3+jsq+lV0tyTUcPw7/+Y9Of/j++/ozd999pavBBDoZHIgxsFfivvvg778hNVUvFRg1So9i33hDB18EBWn31fDhOsJxx47CFXguAuGBjk02sfnwZkAn1ndnrJHEZbU2bMbZDJbsXELfOX3Jzs1m8ZDFBFcpX+u8Af2ZeeghqFcPnn5aV7b59lt4zQVZq6KiwMvLzMMaHIKZgy0stWtrV/HNN+vXZ87A+vWwahX88Yf+Qpg5U58LCtJLCjp3hi5d9FNxCaIfIwIimL9lPjl5Ofh4lfxPZk2R6M4jWNAG9v3499mTsefCXLQns+/EPuJS4vS2L46kQ0koFFV8q7BoyCKaBzV3tcTSQyn49Vd480344QedrP+OO+DRR6FtW9fp8veH5s2NgTU4BGNgi0vlytpVfM01+nVenn4StxrcVatg4UJ9ztdXz+106aK3zp11oedCEhEYQa7KZf/J/YQFhJVYeuKhRAIqBTglSYQjiQ7V+WE3pm30OAObm5fLlvQtFwzqqn2rLhSR96/gT+f6nbm5xc10bdCVq+peRZUKVVysuJQ4cwa++ALeegu2bNGfgxdfhAceKNJnwqnExsIvv7hahaEMYAyso/Dy0q7iFi10jUqA9HRtbK0G9513dLAUaDeYdYTbpYu+zsu+x942kthRBrZNSBvEWQv2HUSrWq3wEi82pm3kphY3uVrOFVl3YB2/7v6VuJQ4/tj3ByfOnQCgTtU6dG3QlSc7PUnXBl1pHdLaIZ4IjyI1Fd59Fz78UMctREXBrFkweDBULFoku9OJjdWF3FNToU45L2hvKBHl7FNeygQHw4036g3g3DkdPGE1uEuW6A8yQECADqKyGtwOHcBP5561TTbRM7xniSTlqTySDicxrO2wEvVTGlT2rUzzoOYeURt2a/pWrvroKgAigyMZ3GowXRt0pUv9LoQFhLn9w4zTiI/XbuB58yA3V38WxozRnh93/Z3EWFKix8frOAuDoZgYA1uaVKz4z9zsk0/qeahdu/4xuFajC+Djo5/yu3ShfqeO1M/0Ivl4ySOJ92bsJet8ltvPv1qJCo0qVPk8V/PL39qluP2h7TQLauZiNS4mJ0fHJLz5pv6frloVRo+Ghx/WEcLujjXQKSHBGFhDiTAG1pWIQJMmerv7bn3s2DG93tZqcKdPx/utt0gB0j97E/61559RbqtW4O1dpFtaA5yulCLRXYgKiWJO0hyOnTlGjco1XC2nQJbtWUbjGo3Lt3HNyICPPoJp0yAlRRvTN9+Ee+6BatVcra7w+PlBy5Ym0MlQYi5rYEXkN0ABx5RSt5SOpHJOjRpw/fV6A8jOho0bee/NO2m2I51ev/0Gc+boc1WrQseO/wROdeyoj10Gq4FtVSt/aV73xLY2bI/wHi5WY5+cvByW71nO4MjBrpbiGv76C95+W8+pnjoF3bvr1/36FfkB0G2IjdXeJKXc15VtcHuuNIIdZvmZ62QdhoLw9YX27dk4uBsTt3/L4ScP6ATo1hHuqlU6ClMp7dZq0+Yfg9ulCzRocNEXROLhRBoFNsK/gr8L31ThaRuql2xsSNvgtgb2z4N/cvLcSafmZnY7lIKlS/UIdfFivczm9tv1/GpUGUhtGRurHxgOHNDrcw2GYnBZA6uU2ltaQgyXJyIwgvTT6WSez6JqWBiEhf1TdPrkyYvdyrNm6YhNgLp1LzK4W1M30SbUM+ZfAWpVqUWdqnXcOqPTsuRlAHQP6+5aIaXBmTMwe7Y2rFu2QK1auj7ryJEQWoZq1toGOhkDaygmxZ6DFZEkpZRnTOSVAWyr6lwSoFStGvz733oDHWSSlHTxKPd//wNgnS8cbpUNSS9ow9upk45gdmOiQ6NZuXcl53LOFbk4QWmwLHkZrWu1plaVWq6W4jzS0vRD2wcfwJEjOhnEJ5/oZTbOLCHnKtq21e7thAQYMMDVagweypXmYAcWdAq47OOqiFQCVgIVLfdZoJSakK9NReAzIAY4CgxSSu0plPJyRniAXgubfNyOgc2Pj4+ubRkdraM3Afbt4+9Fn7N41vMMPeUFU6fqZRMiEBl5sVs5IsKt5p0e7vAw182+jim/T+HFHi+6Ws5FnMs5R1xKHPfH3O9qKc7jxx9hyBAdxNS/v3YDd+vmVv8jDqdyZR1EaAKdDCXgSiPYecBsdKBTfq702HoO6KmUyhIRXyBORJYopdbYtBkOHFdKNRaRwcCrwKBCai9XlLjwev36rOgYyqOHoc/oHwmsWBvWrftnhDt3rk4CADqjjm0SjHbt9Bybi7i28bXc2eZOXol7hVsjb3WrAK01+9dwJucMvcLL4PxrXh5MmQLjx0Pr1no5WfNylM4xJkZnYzOBToZiciUDmwj8n1Jqc/4TItL7chcqnaE9y/LS17LlN9Q3AhMt+wuAd0REVFnN7l4CalSuQbWK1UqU9D/xUCJ+vn7aWHt5Q8+eegM9mt269R+D+8cf8M03+lzFitC+/cWpHms6t1xdfv577X/5cdePjFg4glX3rsLbyz2iU5clL8NLvLim4TWuluJYMjLgrrt0xag779QPX5bEJ+WG2FidX3zfPh0saDAUkStV0xkDnCzg3BVz14mIt4hsBA4Dvyil1uZrUhfYB6CUygFOAKX7ze0hiAjhAeElKluXeCiRVrVa2TdO3t56lDJyJHz++cUVhB58UC8Xsq0g1Ly5UysI5SfIL4g3r32TtQfW8t7695x6r6KwNHkpsXViqV6puqulOI7Nm/UD1ZIlek3rZ5+VP+MKFwc6GQzF4LIGVin1u1IqpYBzV/yvU0rlKqWigHpABxHJ79uz53e55JtaRO4XkXgRiU9PT7/SbcssEYERxR7BKqV0DuJaRYggtlYQeuMNHaV84gSsWKHdhk2a6Gw9w4drYxscrI3vq69CXBycPVssnZdjSOshXNf4OsYuHXshcb4ryTqfxdoDa8uWe/jLL+GqqyArC377Tc/hl1f3aJs2Op7B1IY1FJMrBTmNv8xppZSaXJibKKUyRGQ5cB1g627eD9QH9ouID1AdOGbn+unAdIDY2Nhy6z6OCIxgya4lKKWKnNs2LSuNo2eOlixFYmEqCH3/vT5XwgpC9hARPuj7AZHvRTJy0UgWD1ns0hy/cSlx5OTllDg/tFuQnQ3PPAP//S907aqjzmvXdrUq11KpkvbqmBGsoZhcyUV8ys6m0MFJz1zuQhEJFpEAy35loDewPV+zhYAlRyC3AMvM/GvBhAeEczbnLGlZaUW+1ikpEq0VhEaM+MdVfPiwHtk+9ph2O0+bBgMH6jWSjRvrlJDTp2s3ZF5ekW/ZMKAhU3pNYcmuJczdPNdx76UYLN29lAreFehcv7NLdZSYQ4egd29tXB95BJYtM8bVSkyMNrDma8lQDK6UaOJ1676IVAUeBe4FvgReL+g6C7WBT0XEG23I/6eUWiQik4B4pdRC4GPgcxHZhR65ltNcc4XDNpK4dtWifQFeMLC1nLx0ubgVhDp31hWEqly5LupD7R9iTtIcHv3xUf7d6N8E+QU58Q0VzLI9y+hcvzN+vh48P7l6NdxyCxw/ruu0WpOXGDSxsTq/8t69OrmLwVAErphoQkRqAI8DdwCfAu2UUsevdJ1SKhGItnN8vM3+WeDWogguz9ga2C4NuhTp2sTDidStWpeafqUcQ1aUCkLe3nrtru2a3Lp1L+nS28ubj/p/RPSH0Tz+0+N8dtNnpfuegGNnjrHh4AZe7O5e63ILjVLw/vt6TWv9+trQtm3ralXuR2ys/hkfbwysochc1kUsIv8B1gOZQGul1MTCGFeDc2gY0BCgWJHE1iLrLsdaQcjqKt6yRRfgXrRIzwFWqaKPDxqkU9RZU0K++y5s3KiXE6GLFYztOpbPEz/np10/lfrbWLFnBQrlmfOvZ87AsGHw0EPwr39p42GMq31atdLxBGYe1lAMrjSCfQKdMOIF4HmbgBJBBzl5UA0qz6eSTyXqVq1b5Eji7NxstqVvo0/jPk5SVkJq1IC+ffUGOuBmw4Z/Rrn2Kgh17swLHa9isX8THlj0AJsf3FyqBQyWJi+lim8V2tdtX2r3dAjJyToyfMMGmDgRxo3Tc+kG+1SsqKOJHRlJfOKEjk4uxHSIwbO50hys+eS5GcVZqrPj6A6y87LdYwRbGHx99Xxshw7ahanUpRWEJk2iglLEe3mxsVYeG5d2oevtz9itIOQMliUv45qG11DB23UZrorMTz/pijdKaY+B9YHGcHliYnRUtSMyOuXm6riD48d1LufrrnOMRoNbYgyohxEeWPRkE6UW4OQsRP5xFb/3HmzapL+gfvoJeeEFKofUI+qnRH0+LEzPKd52G7z1lo5qdjAHMw+y7cg2z3EP5+XBSy9Bnz76dxMfb4xrUYiN1Zmtdhc/i9oFvv4atm3TxrpPH/0A6YQ14wb3wBhYDyMiIIIDJw9wNqfwH8rEQ4n4evnSLKiZE5WVMtWr6+pBL75InbVbiHy5DreNbUzOW/+Fq6/WiTHGjNFfYsVYDnQ5rOXpPMLAnjihq8GMG6cT9q9eDY0auVqVZ2ENdCqpm1gpXWSjaVOdKW30aP0Q2L69rn5lKHMYA+thRARGoFDszSh8qd7EQ4m0CG7hWe7MIlCtYjWm9X+f+RV38WrUKV24ICUFPv4Y/vxTjxocyLLkZQRWCiQq1M0Li9umPHz7bZ0CszymPCwpkZG62EVJA51++UX/Pz79tJ5/nTZNF6s/fFj/nd56y+EPgwbXYgyshxEeaClbVwQ3cdLhJM+Zfy0m/Zv157bI25i0chLbj1jymdx9t06EMX78hehjR7BszzJ6hPfAS9z44zNvnk55mJmpg8Qefrj8pjwsKRUq6Cjrko5gp07Vy87uvPOfY9dfr0evvXtrj8v118PBgyW7j8FtcONvCIM9ilq27tiZY+w/ub9oOYg9lLeve5sqvlW47/v7yFN5el3tpEl6zmv2bIfcY/fx3ezJ2EPPMDd1D2dnw+OP60Lo0dF6xNS1q6tVeT6xsdrAFneEuXatftB5/HEdmWxLrVo6xeh77+lc323a6DJ5Bo/HGFgPI9Q/lEo+lQptYJMO6bkdh6ZIdFNC/EN449o3iEuJY3rCdH1w4EBtaCZOhPPnS3wPt55/tU15+PDDJuWhI4mJ0fPZf/9dvOunToXAQLjvPvvnRWDUKP1AVK+ezoQ2ahScPl18zQaXYwysh+ElXoQFhBXaRWyNIC7rLmIrd7e9m17hvXj6l6c5cPKAXuP50kt6/efMmSXuf1nyMmr716Z5kJsVHl+zBtq1g/Xr9Vzr229r16bBMZQk0GnbNp2f++GH9Truy9Gihf5bPvkkfPCB/pv++WfR72lwC66YKtHgfhRlLWzioURqVq5Jbf/yMZIRET7s9yGt32/Ngz88yLeDvkX69NGpFydP1vOylSsXq2+lFMuSl9E7ordLq/hchFL6i/jRR03KQ2fSsqV27cbHa/d7UXj1VR1c9vDDhWtfsSL85z96jexdd+nEKi+9pI2ui5OCJCQk1PLx8fkIaIUZoOUBm3NyckbExMTYXQ9oDKwHEhEQQVxKXKHK1lkDnNzGIJQCjWo0YlKPSTz1y1Ms2LqAWyNv1TVsu3fX+Xcff7xY/W5N38qhU4fcxz185ox2I376qQ6O+eIL7YY0OB5fX4iKKnokcUqKnv9/6CEIKmJRil69IDERHnhApxH98UddKKNevaL140B8fHw+Cg0NbREcHHzcy8urXJcYysvLk/T09JZpaWkfAf3ttSnvTyAeSXhgOCfPneT42cunhc5TeeUigtgeYzqOITo0mid+fkKvGe7WTefdfeUVHVlbDKzzr25RYD05WWet+vRTmDBBB8kY4+pcYmO1u7YogU6vW4qOFfOhjpo1Yf58veRs3Tp47bXi9eM4WgUHB58s78YVwMvLSwUHB59Aj+bttylFPQYHUdhI4t3Hd3M6+3S5NLA+Xj68/u/X2XdyH++ue1cffOklOHJErzcsBsv2LCMiMOJC0QWX8dNP+st+925tWCdOdLnrsFwQE6MfznbuLFz7I0dgxgydYaxBg+LfVwTuvVcXu5gypfj9OAYvY1z/wfK7KPDDZz6VHkhhDazHp0gsIT3Ce3Bd4+t4+feXyTiboXMb33gj/N//6VSLRSA3L5fle5a7dnlOXh68/LLOTlW3rnZX9uvnOj3ljaIGOk2bpt34zzzjmPs3bgz+pVfQwl3ZsWNHhSZNmkTaHnv88cfrjB8/PsRVmgrCaQZWROqLyG8isk1EtojIo3badBeREyKy0bKNt9eX4WLCAyzJJo5fPpI48VAighBZK/Ky7coyU3tNJeNsBq/GvaoPTJ4MJ0/qIJIisCFtAxlnM1w3/3riBNx0E7zwgk7Yv3q1/sI1lB4tWugAucLMw2ZmagM7YIC+zuCx5OXlkVvMRDXOHMHmAE8opVoAHYGHRKSlnXa/K6WiLNskJ+opM1StWJUgv6BCjWCb1GyCn2/5TY/XNrQtd7S5gzfXvqmX7bRuraNA33oL0tIK3Y9L179aUx7+8AO8+aYOZjKlzkofH5/CBzrNmKG9JM8+63xdhgt06NCh2b333ls/Ojq6eZMmTSJ/++03P9Aj3AEDBoR37NixacOGDVu9/vrrFyLOxo0bF9KqVasWTZs2bfnYY4/VAT1KjoiIiLzzzjsbREZGtvz777+LtebNaVHESqmDwEHLfqaIbAPqAluddc/yRHhAOLszLm9gkw4nuX++3FJgco/J/G/L/5i4fCIz+s+AF1/U5cdeeaXQ87FLk5cSGRxJiH8pe6HmzdPzb1Wr6sQRV19duvc3XExsrC4zl5urM4XZ49w5HdzUo4dOV1lGufde6m/ejEOf3lu14vTMmewrSR+nT5/22rBhw/YlS5b433///eE7d+7cArBt27bKCQkJ2zIzM72jo6Nb3nzzzSf+/PPPyrt27aqUmJi4TSlF7969Gy9ZssQ/IiLi/J49eyrNmDFjzxdffJFSXC2lMgcrImFANLDWzulOIrJJRJaISPn1ZRaRiMCIy7qIs85n8fexv8tFisQrERYQxoOxDzJz40y2pW+DJk1g2DC9fjTlyp+d87nn+X3v76U7erVNeRgVpaNXjXF1PbGxkJUFf/1VcJsvvoDUVDN6dRIFLTm0Hh8yZMgxgD59+mRlZWV5HTlyxNvyOsPf31/Vrl07p1OnTid///33Kj/++GO1lStXVmvZsmVLy0i10vbt2ysB1K5d+3yvXr1OlUSr09fBiog/8BUwRil1Mt/pP4GGSqksEbke+BZoYqeP+4H7ARqUJBqvDBERGMFX274iJy8HH69L/4xbDm9BocplBLE9nr/meT7e8DHPLXuObwZ9owsAfP65npOdMeOy167dv5YzOWdKb3nOoUMwaJDOSzt6tB4NmaxM7kFMjP4ZH29/bjU3Vy+liY7Wy8LKMCUdaRaXkJCQnBMnTlzkPjh27Jh3eHj4ObjUAFtf2zuulGLMmDEHn3rqqSO253bs2FHBz8+vxKWNnDqCFRFftHGdrZS6pGaYUuqkUirLsv8D4Csil6zGVkpNV0rFKqVig4ODnSnZYwgPCCcnL4f9J/fbPX8hgrgc5CAuDEF+QTzT5Rm+3f4tf+z7Qy+beOAB7e67wrKLpclL8RIvuoV1c77QNWv0l/jatTqpwLRpxri6E82b66xMBUUSf/ONHt2OHWuqFzmJ6tWr59WqVSv7u+++qwpw6NAh7+XLl1fv2bNnFsDcuXMDAX766Sf/qlWr5tasWTMXYMmSJQGnT5+WtLQ07zVr1lTt2rXrqT59+pz8/PPPg06cOOEFkJyc7HvgwAGHDTydGUUswMfANqXUGwW0CbW0Q0Q6WPQcdZamsoR1qU5BbuLEQ4n4V/AnLCCsFFW5N2M6jiHUP5Rnfn0GpRQ895w2XhMnXva6ZcnLaFe7HQGVApwnTimdZeqaa7Sm1ath6FDn3c9QPLy99ejUXqCTtaB6kya6yITBaXz66afJU6ZMqd28efOW3bp1a/bMM8+kRkZGngMIDAzMjY6Obj569OiGH3744R7rNdHR0ad69erV5Kqrrmrx5JNPHgwLC8seOHDgyVtvvfVY+/btmzdt2rTlTTfd1CgjI6OAyfWi40wXcRdgKJAkIhstx54DGgAopT4AbgFGiUgOcAYYrJQyi5gLge1a2B7hPS45n3Q4ida1Wrt3zdJSpkqFKkzsNpGRi0ey6K9F3NDsBnjkEe3SGzsWWl2akGMLKqcAACAASURBVOXU+VOs2b+GxzsVMxNPYbBNeXjddTq1Xo0azrufoWTExupphfyBTr/+qke2M2YUHABlcAgxMTFn165da3cifNCgQcfffffdA/mPN2nS5OzcuXP35j8+bty4w+PGjbskl7A1OKokOO3bVykVp5QSpVQbm2U4PyilPrAYV5RS7yilIpVSbZVSHZVSfzhLT1mjfvX6eIu33aU6SikSDyWa+Vc73Bt9L01rNuXZpc+Sm5cLTz+tI3THjbPbPi4ljuy8bOcEOO3dq42qNeXhuHGwaJExru5ObKwuI7d9+8XHp07V5QGN58FgwST791B8vHxoUL2B3bJ1BzIPcPzscWNg7eDr7cuUnlO4Zf4tfLbpM+6JvkdXKRk/Xpd6a9/+ovbLkpfh6+VLl/pdSn7zPXtg+XK9rVihXwOEhOgC2zfcUPJ7GJyPbaBTpGXhw7p1ehnVf/5zaUF1Q6mxbt26HfaOv/HGG6mlrQVMqkSPpqCydeU9ReKVGNhiIB3qdmD88vGcyT4DY8boSicvvHBJ22V7ltGxXkeqVChiYgeldK7gTz7RJfIaNoTwcLjnHj1KbddO12zdtEkv6TDG1XNo2lSnLLQNdJo6FQICdOCcwWDBGFgPJjwg/PIG1kQQ20VEeK33a+w/uZ931r2jXcTPPgs//wwrV15od/zMcf48+GfhlucoBX//raue3HWXNqiNGukkET/8oEfG06ZBUhIcPgxffaXrg7ZpYxL1exre3voByRrotG2bjh4ePfrKBdUN5QrjIvZgIgIjSD+dTtb5LPwr/JMEPOlwEg2qN3Bu1KuH0y2sG9c3uZ4pcVMY0W4EgQ8+CG+8Ac8/r42sCCv2riBP5dmff7UaVKvLd/lyOGCJqwgO1rVnn3lG/2zRwhjRskZMjE5UkpOjg+QqV9YBcwaDDcbAejC2S3VsR6smwKlwvNLrFaI+iGJq3FRe/der2kX84IO6HNx117EseRl+vn5cVe8qbVB37vxn/nT5cu3aBahVSxvS7t113dkWLcwayLJObKyO/v75Z525adQo/WBlMNhgHqs9mPBAS1Udm0Cncznn2H5ku0mRWAjahLRhaNuhvLX2Lfad2AfDh0NYmDa0eXnsXrOEV3aFUeHOu3V5uGbN9Bzb0qU6beH772v3YFqazhk8ahS0bGmMa3nAGug0cqR++HriCdfqKUeISMyAAQPCra+zs7MJDAxs26NHj8YAs2fPrv7cc8+F2rvWz88v2t7xm2++OeyTTz4JBF0wYOXKlQ7JsWxGsB6Mvbqw249sJycvx4xgC8mk7pP4cvOXTFw+kY9v/FgnnRg2jLygIBZZa8bWPq5HptZRatOmxoiWd5o00fOt+/bpZTkNG7paUbmhcuXKeTt27KiclZUl/v7+6ptvvqkWEhKSbT1/xx13nABOuFDiBcwI1oOpWbkmVStUvcjAmgCnotEwoCGj249m1qZZbDm8Be68E26/nZROLbi/HyStXKDnVufO1aPXZs2McTXoOXXrKNZRBdUNhaZXr14n5s+fHwAwd+7cGjfffPMx67m333675l133dUAYPv27RWioqKat2rVqsWjjz5ax9omLy+Pu+66q0GjRo0iu3fv3vjIkSN2B5tff/11taioqOYtW7Zs0adPnwhrSsXCYkawHoyIEB4YfpGLOOlwEhW8K9C0ZlMXKvMsnrv6OT7a8BFjl45l4e0LYc4cXl54Hwu2beX9LgOMQTXY5/HHdeatyPJZBOze7+6tv/nwZseWq6vV6vTMG2desYjA0KFDj02YMKH2oEGDMrZt2+Y3fPjwo3/88Yd//nYPPvhggxEjRqSPHj366CuvvHJhkvzzzz8P2LVrV8UdO3Zs2b9/v2/r1q0jhw0bdlGa3oMHD/pMmTKl9sqVK/+qVq1a3vPPPx86efLkkP/7v/87WNj3Y0awHk7+tbCJhxKJDI60W2HHYJ+afjV5tsuzfP/X9/y+93dAJ/jvHtYdby+T8s5QADfcYEavLuKqq646s3///oozZsyo0bt37wLdwX/++af/fffddwzggQceuGBAV6xYUfW222475uPjQ1hYWHanTp0y81+7fPnyKn///XelDh06NG/evHnLL7/8smZKSkqRKm+Yb2EPJyIggp92/YRSChEh8VAi/270b1fL8jge7fgo09ZN45lfn2H2wNkkZyTzWMfHXC3LYHBbCjPSdCbXXXddxoQJE+r//PPPOw4fPlygLfPy8rKb376gurJWlFJ07dr15Pfff19w4e0rYEawHk54YDhncs5w6NQh0k+lczDroAlwKgZ+vn682P1FVu9fzZifxgCUboF1g8FQJEaNGnXkiSeeSO3QocOZgtq0a9cua8aMGTUAZsyYUdN6vFu3bpnz58+vkZOTw969e33XrFlzSYaQ7t27n4qPj/ffvHlzRYDMzEyvxMTEIuXBNAbWw7GNJE46nASYFInF5Z7oe2hWsxkLdywkpEoILYNbulqSwWAogEaNGmXbq4Jjy3vvvZcyffr0Wq1atWphW6R96NChGREREeeaNWsWOXz48AYdOnS4xEVcp06dnA8//HDP4MGDI5o2bdoyJiameVJSUqWiaBRPqw4XGxur4u3VYiynbD+ynRbvtuDzmz7nyOkjPPbTY6Q9kUaIf4irpXkk32z7hoH/G8jtrW5nzs1zXC3HYHAYIpKglIotSR+bNm3a07Zt2yOO0lQW2LRpU1Dbtm3D7J0zc7AejrWgevLxZPZk7KFWlVrGuJaAAc0HMKHbBPo17edqKQaDwcNxmotYROqLyG8i/9/emcdJVVx7/HuGYdgGWQYEZJUAAqKI4IKCAZcniop+jD6XLK5EFJfEmJiYGLenPI2APJ9bfBqJUQm4IWJEEFAIENlBQAYBBUT2fR/mvD/OndA03T0Lvc/5fj716dv31u/W6e7b99yqOlUli0XkSxG5O0IeEZFhIrJMROaLyKmJsidbqZ5bneNqH8fyrcuZv96nSDxaRISHej1Et+OO6kHfcRwnoX2wRcC9qtoBOBO4Q0TCO7UuAtoGqT/wfALtyVpa12tN4aZCFq5f6FMkOo7jpAkJc7CqulZVZwfbO4DFQNOwbP2A4WpMB+qKSJNE2ZStHF/3eGasmcHeor1eg3Ucx0kTkhJFLCKtgC7AjLBDTYHQsVSrOdIJO6XQul5rioqLAJ8i0XEcJ11IuIMVkXzgbeAeVd0efjiC5IiwZhHpLyIzRWTmhg0bEmFmRlMyVCdHcnxoieM4TpqQUAcrIlUx5/o3VX0nQpbVQPOQ982A78IzqepLqtpNVbs19DUXj+D4urZy0wkFJ1A9t1zDtBzHcTKKaEvOpSOJjCIW4P+Axao6OEq20cBPg2jiM4FtqlrmiZQdo6QG6/2vjuM48eXAgQOlZ4pCImuwZwM/Ac4VkblBulhEbhOR24I8Y4HlwDLgz8DtCbQna2lSuwmdju1EnzZ9Um2K4zhO0lm6dGle9+7d27Vr165j9+7d2xUWFuYVFRXRrFmzk4qLi9m4cWOVnJycrh999FE+QNeuXU9YuHBhte3bt+dcddVVrTp16tShQ4cOHV9//fW6YEveXXTRRa3PPffcNj179qzw0mQJm2hCVacQuY81NI8CdyTKhspCjuSwYMCCVJvhOE5l4qabmrMwvsvV0anTbl4p/yICt912W4vrrrtu05133rlp6NChBQMGDGg+fvz4r48//vi9s2fPrl5YWFitY8eOuydNmpTfq1evXd9//31ep06d9g0cOLBp7969t48cOXLlxo0bq3Tr1q3DZZddth1sJZ758+d/2ahRo4MV/Tg+F7HjOI6T0cyZM6dW//79NwMMGDBg86xZs/IBzjrrrB0TJkyoPXny5Nr33Xff2mnTptX+7LPPanXu3HkXwKRJk44ZMmRIk/bt23fs0aPHCfv27ZNly5blAfTs2XP70ThX8KkSHcdxnIpQgZpmsunVq9fO5557ruG6devyBg8evGbIkCGNJ0yYULtHjx47wJakGzVq1LLOnTvvC9VNmTKlVs2aNYuPtnyvwTqO4zgZTZcuXXa9/PLL9QBefPHF+t26ddsJtuTc7Nmz83NycrRmzZp64okn7h4+fHjD3r177wTo3bv39qeffrpRcbH50qlTp9aIp11eg3Ucx3Eyhr179+Y0anRoyMSAAQPWPf/889/+7Gc/a/XMM880LigoKBo+fPhKgBo1amjjxo33d+vWbRdAz549d44ePbp+yRqygwYN+q5///4t2rdv31FVpVmzZvsmTpy4LF62+nJ1juM4lQBfri4xxFquzpuIHcdxHCcBuIN1HKdURo2CTz9NtRWOk1l4H6zjODFZtAiuvRZq1oQlS6CJr3flOGXCa7CO40RFFe6+G2rVgr174Ve/SrVFToopLi4ujjmBUGUi+C6iDudxB+s4TlTeew/Gj4dHH4X774c33oAJE1JtlZNCFm7YsKGOO1lzrhs2bKgDLIyWx6OIHceJyJ490LGj1V7nzoUDB+CkkyA3F+bNg2rVUm2hUx7iEUU8a9asY3Nzc18GOuEVtGJgYVFR0S1du3ZdHymD98E6jhORP/0JVq604KbcXEvPPgsXXWTHHngg1RY6ySZwJJel2o5MobI/gTiOE4FvvoEnnoCrroLevQ/t79MHfvQjeOwxWLEidfY5TibgDtZxnCO47z57feqpI48NGWK12TvvtCAox3Ei4w7WcZzDmDgRRo60oKaWLY883qwZPPwwfPghvP9+8u1znEzBg5wcx/k3RUXQpQvs3GnjX2tEmfr8wAHo2hW2brV8+fnJtdMpP/EIcnLKR8JqsCLyioisF5GIIcwi0ktEtonI3CA9mChbHMcpG88/DwsXwuDB0Z0rQNWqlnfVKnjkkeTZ5ziZRCKbiP8C9Cklz+eqekqQ/G/qOClkwwZ48EE4/3y4/PLS8599Ntx8s/XJLow6EtBxKi8Jc7Cq+hmwOVHndxwnvvz+99Y0PGwYSBmnERg0CI45Bm6/3QOeHCecVAc5dReReSLykYicGC2TiPQXkZkiMnPDhg3JtM9xKgWzZsGf/2yRwR06lF3XoAE8+SR8/jkMH544+xwnE0lokJOItALGqGqnCMeOAYpVdaeIXAw8o6ptSzunBzk5TnxRteber7+GpUuhTp3y6YuLoUcPKCyEr76C+vUTY6dzdHiQU/JJWQ1WVber6s5geyxQVUQapMoex6ms/O1vMG2aTSxRXucKkJMDL7wAW7bA734Xf/scJ1NJmYMVkcYi1tMjIqcHtmxKlT2OUxnZsQN+/Ws47TS44YaKn+fkk+Guu+Cll2DGjLiZ5zgZTcLmIhaRN4FeQAMRWQ38EagKoKovAD8CBohIEbAHuEbTbFDuzJnw9NMwdSo0bGjrYEZLjRtDXl6qLXac8vHYY7B2Lbz7rtVEj4aHH4YRI+C22+CLL2y2J8epzPhEE2EUF8OYMeZYP/sMateGvn1h2za7Ea1dC+vXR46YLCg43OleeCFcd13CTHWco2LpUujUCa6/Hl59NT7nHDkSrr4annnGarRO+uB9sMnHHWzAnj0WBTl4sN14WrSwhaZvucWGIYRSVGROtsThRkrffgvff2+RmbfcEndzHeeo6dsXpkyx671Ro/icU9VW2/nnP2HJEjjuuPic1zl63MEmn0rfiLN+Pfzv/8Jzz8HGjdCtG7z5pq0YEq2JKzfXbhyxbh7798Nll8HPf27Ny/36JcZ+x6kIY8bA2LHWUhMv5wo2fvbZZ61mfO+99l9ynMpKqsfBpozFi+HWW62m+uijcNZZMHky/OtfcM01R99/lJcHo0aZw77mGhsn6DjpwL59cM890L49DBwY//O3aQO//S289RZ88kn8z+84mUKlcrCqtnh0377QsSO8/rpFTi5ebKuCnHNO2WewKQv5+bbiSMuWcOmlsGBB/M6daRw8aEM4OneGOXNSbU3lZsgQG/M6bFjiAvN+8xtztHfcYQ7dcSojlcbBfv65rf5x3nkW4fjww9ZP+sILcMIJiSu3QQP4+GOoVcuCnlauTFxZ6cqOHXDFFTbOcsUKm9TgrbdSbVXlZM0aixy+/HK44ILElVO9unW9FBbaw6U/VDmVkUrjYKtXh717Lejo229tUvOGDZNTdsuW5mT37DEnu3FjcspNB1auNIc6dqz1zRUWwqmnwrXXWjPiwYOptrDyMG4c9OljQXpPP5348v7jP2DoUHugPfVUuOoqW9rOcSoNqppRqWvXrlpRiosrLI0Ln3+uWr266mmnqe7YkVpbksGUKaoNG6rWras6btyh/fv2qd56qyqoXnyx6tatqbOxMjBrlur559v3ffzxqqNHJ7f8LVtUH3xQNT9fVUT1xz9WLSxMrg2OKjBT0+AeXplSpanBQnz7VytCjx42EH/WLLjySos0zlZeew3OPRfq1oXp0w9vjszLgxdftMjtcePgjDNsDlsnvqxYYWNcu3a1JtqhQy3e4NJLk2tH3brWJbNiBdx3H7z9tgVY3XqrtSY5TtaSag9f3nQ0Ndh04eWXVUH1+utVDx5MtTXxpahI9de/ts933nmqmzbFzj9pkmqDBqp16qiOHZscG7OdjRtV77lHNS/PWkx++9v0aiVYu1b1rrvMvrw81YEDVb/7LtVWZT94DTbpySeaSBGPPw4PPAC/+IX1h6W6dh0PduywGtMHH8CAATabT9Wqpeu++cbGCc+fb+uL3ndfcr8PVVi3DpYvt7RypQ3TKiiwlWEKCg5P1aolz7bysHu3feeDBtm6rjfeaDXHpk1TbVlkVq2ygKtXXrHve+BAmxc5WbERlQ2faCL5uINNEao2FnHYMPjv/7YbSyazcqVNrLFokd3k77ijfPpdu+Cmm+Dvf7fpJV9+GWrUiJ99u3ebjSVONDzt2VP2c9WseaTTrV8fjj0WevWCnj2TOw/vwYPWJP/ggxYlfMkl5mRPjLrCcnrx9dfwyCM2bK5mTftf3HuvNS078cMdbPJxB5tCiovNmYwYYXPBHs1qJqlk6lQbhrN/v81FW9HhH6rmGB54ALp0gffeg+bNy3eOgwfhyy9tRZcZM2y6vuXLbfrKUGrVgh/8AFq3PjK1bGm2bNp0eNq8Ofa+zZvtN61f3/o5r7jCImnj+aAQiqqNs77/fvvMp58OTz1l47kzkcWL4aGH7CGrdm37HfLyLFWrdvhrtO1q1Wxq02OOsaX3Ql9LtmvVOvqFDTIRd7DJxx1sitm3z2ocEyeaQ7nkklRbVD5eew3697cZsT74wIJXjpYxY+zBo0YNC4jp0SN63rVrLYiqxKHOnGnNo2CO7qSTIjvSBg3i3wy9a5cFbb37rn0XW7dajaxPH3O2fftCvXpHV8a2beZMFyywaQgnT7YJHZ54wgLnsqGrYd48G9K1ebP9P/bvP/Ra2vaBA6WfX8QceKgDLiiw77FdO0tt21rTejY5YnewyccdbBqwYwf07m3Nq+PH27SN6U7JzExPPmnRwiNHmkOLF4sXW7/sypU2YcGtt1oz7+zZ5khLnOqqVZY/NxdOOcUiks88017btEmdwzlwwJzfu+/ag9N335mNvXqZs+3XL3bf6J49VvtesAAWLjyUSj4v2BzCf/iDPeCUpa+7MnDwoD1gbdsG27dbirQdvm/9eli27PCugho1zNG2bXu4423XLjEPaInGHWzycQebJqxfbzW1jRutubhdO2jWzJ6004m9e83JDRpU/mCm8rJli01I8fHHNtvWsmWHJqZo1cqcaIlD7dLFJhNJR4qLbbKFd9+1tHSp7T/jDJtRqXdvc5yhznTZMtOBNX126GAT6J90kr126mStBpl2k09niovtQWjpUkuFhYe2ly+3CTpKqFvXnG2TJof/BuG/R7RjOTlQpYo9dFWpcvh2pH0l2z162Gx0FcEdbPJJmIMVkVeAS4D1qtopwnEBngEuBnYDN6jq7NLOm60OFg5NIxjaX1i7tjnaZs2sxhNpu6AgcTfaVatg2rRDac4ca46rUqViwUzl5eBBW4xh2jRbOKHEqcZzBZhks3jxIWcbeinn5FitO9yRtmnji5enmqIia00Jd7wbNhzKE34rDX0fvl1cbNf2wYN27vDtSPtUbY7nQYMq9hncwSafRDrYc4CdwPAoDvZi4E7MwZ4BPKOqZ5R23mx2sGDNVfPmwerVFhG6evXh22vXHqrZlFCt2iGHG/5ast24cek36ZLa6fTphxzqmjV2rHp1c3Ddu1s666zMdnLpwqpVtoJT69bWf52ogCgn8ykuNidbpUrF9O5gk0/CnotV9TMRaRUjSz/M+SowXUTqikgTVV0bQ5P11KkTOwq0qMjGbEZzwDNmwDvvHLmCSU6ONWeFO96CAmuSDK2dgjXBnnPOIYd68smJW3mlMtO8efkjpZ3KSTYFXFUWUtnw1BQICdlgdbDvCAcrIv2B/gAtWrRIinHpSm6uOcZYATIlQ0yiOeElSyyYavt2y19SO7377kMOtXHj5Hwex3GcbCWVDjZSr2HE9mpVfQl4CayJOJFGZQMiFuXYoIFF1kZjxw4Lrmre3GunjuM48SaVDnY1ENo41gz4LkW2VEpq106/KGXHcZxsIZWt+qOBn4pxJrCtsve/Oo7jONlDwmqwIvIm0AtoICKrgT8CVQFU9QVgLBZBvAwbpnNjomxxHMdxnGSTyCjia0s5rkCCR1E6juM4TmrwwG/HcRzHSQDuYB3HcRwnAbiDdRzHcZwE4A7WcRzHcRJAxq2mIyIbgG+SXGwDYKPrUlZWtusywcZM0WWCjanQAbRU1YYV1DoVIOMcbCoQkZkVmSQ7m3WZYGOm6DLBxkzRZYKNqdA5qcGbiB3HcRwnAbiDdRzHcZwE4A62bLzkupSWle26TLAxU3SZYGMqdE4K8D5Yx3Ecx0kAXoN1HMdxnATgDtZxHMdxEoA7WMdxHMdJAO5gHcdxHCcBJGy5OsdJBSJSB+gDNAUU+A74WFW3VvB8F6jqJ1GOHQM0VNWvw/afrKrzY5yzMYCqfi8iDYGewFeq+mU5bXtcVX9XTs3xQBdgkaouiZGvBbBeVfeKiAA3AKcCi4A/q2pRFN1lwDhV3VtOu84B1qnqVyLSAzgTWKyqH5aiy8d+7+ZAEVAYlF9ciq490I/Dr5PRqrq4PHaHnO9GVX21lPKaAjNUdWfI/j6q+o8YutOx1T2/EJGO2GddoqpjK2Knk1y8BhsBEblQRG4WkVZh+2+KoRERuVpErgq2zxORYSJyu4iU63sWkU/LkKdB2PsfB+X1D26I0XRXiEj9YLuhiAwXkQUiMkJEmsXQDRaRs8v5OeqLyIMickvwnTwgImNE5CkRqVeKtreIPCsi74vI2yIySETalKL5KTAb6AXUBGoBvYFZwbGK8H9RyroaWAK8LSJfishpIYf/EsPGnwPTgOkiMgAYA1wCvCMiN8fQDQtL/wPcXvI+hu69kO1+wKfApcD7InJDNB0wlkP3h0FAX2AGcBqxh4qMAFaLyF9F5GIRqRIjb4ldQ4My/ioijwJPAjWAX4jIUzF0VwMTMaczEDgd+AkwV0ROiqH7DfAWIMC/gC+C7TdF5P7S7I3CwzHKuwt4H7gTWBj8DiU8HkP3R2AY8LyIPAE8C+QD94vIAxW000kiPkwnDBF5HOiB3agvBYaq6v8Ex2ar6qlRdM8BxwJ5wHagGvABcDH2ZH53FF14TUeAdsBXAKp6chTdv20Rkd9jtaA3sJv1alX9RRTdIlXtGGyPAKYDI4HzgetV9YIoupI5oBtiN9E3VXVOpLwhmrHAAuAYoEOw/XfgAqCzqvaLohsENAImAJcDK4ClwO3A46o6MoruK+CM8Npq4MxnqGq7KLrR0T4CcK6q1oqgmQtcpKprg1rGcOB3qvqOiMxR1S5RyloAnIE5kG+ANkFNth4wUVVPiaJbDUwCxgV2AfwJ+BWAqr4WRfdvW0Tkn9hvvCJ4QJugqp2j6EKvk1nAaSW1QhGZF0M3BzgX+BFwDdAJeBe7XiZH0XwZ5KsBrAGaqupuEakKzFHVTlF084Ezg7wNgL+p6oUicjLwgqqeFUW3FDhRVQ+E7c8DvlTVtjHKi3gIaKeq1aLoFgDdVXVn8NA+Cvirqj5ThmvlFOxe8j3QTFW3i0gN7HqOeG9w0ghV9RSSMCeQG2zXxZ7khwTv58TSBa9VgU1AXvA+t+RYFN1o4HWgPdASaAWsCrZbxtDNCdmeDdQKKT9WeV+FbM8KOza3tPKAtsAfgC+xGtwfsZtLJM3c4FWANeUoa0HIdi4wNdiuByyMoVsK1Imwvw5QGEO3Bauh/TAs9cIejmLaGLxvAswC7gJmxyhrdsj2vGi/aQRdbWAo9hDVNNi3vAzXc2h5/ypHeR9jDxcAb5dci0BBuN3RygveNw6+k2nAqiiahcFr9eC3qBG8r4I1Zcf6r5ZUEmqE/SdiXSdLIv23gv/cVzF06zCH1zIstQK+i6FbFPY+H/gHMLgs/7lIv1Usnaf0Sd4HeyS5GvQvqepWEbkUeElERmK102iUaA6IyBequj94XyQiB6OJVPUyEbkCa3b7k6qOFpEDqlraikE1RKQL1oxXRVV3hZQftTxgkog8AjwRbF+uqu+JSG9gWwydBucvBB4FHg1qCtdiDyGRmm9zgppZbSBfRFqp6koRKSD2d1ksIvVVdTNwHHajRVW3xGr+Bv4LmC0i47CHFIAWWI350Ri66cBujVDDCmrFkdghIj/QoP9VrSbbC3gPODFGWcUiUlWt9tQ3pJzqxOiyUdUdwD0i0hV4XUQ+jJU/hM4ish17yKkmIo3Vasx5BN9rFG4BhovIQ9h1MTeondYDfhlDd9jvo6rfY82cw0SkZRTNhyLyOeZgXwb+LiLTsYecz2KUNRb4h4hMBi7CWmIQ6wKJdZ3cA0wQkUIOv07aYE3N0RgD5Kvq3PADIjIphu57ETmlRKdWk70EeAWI2pQN7BeRmqq6G+gaUlYdIGYfgnLNSAAABrZJREFUs5MmpNrDp1vC/kQ/jLD/MaA4hu4j7M8Xvr8xYTWHKPpa2BPtaKyJt7T8E8NSk2B/ATAzhq4q8BDwbZCKgR1YzahFDF3U2k4MzbXYU/864EpgPPAJ1gzYP4buP7Hm03GBjX2D/Q2BN0opsx7WNHkv1nx6DVAvAddJZ6BtlO/3+hi6FkDVCPubAueXsWwB7gBePwr762LNlqXl64AFA12JNW3nlJK/VwXt6Y419wL8IPjtri5DeRcHeS8I2ZcDVCtFl4MFUl2JNWefiT2oxvU6CcpqBjSOcuzsGLqInwFbsu6kRNjqKb7J+2DDCPo3UNU9EY41VdU15TxfLaz5dn0Z83fGbnwvlKecEH0V7I+5uwx562A19k1lyJuvIdGP5bRH1GryuVgT2xpVXVuKrj7QGlim5YwAFpFGhESHquq6ROmSWVa265JtY5RzVfQ6zwidk1zcwUZBRLoREvqvMYY0uC49yhKRU4AXsD7X1VhNrxmwFbhdVWdH0XUBng90JQ9QMXVhZYVrBmiUALAE6Sr62RJRXrl1ySyrNETkW1Vtka06J7l4H2wYIvJD4GnsT9oVmArUE5EDwE9UdZXryqZLto3Y8Jifq+qMsPOdCbyKNetG4tUK6GKV9ZcYZSVCV9HPlojyKqJLZlmISLQ+ZMECkCIfzBCdkz74ONgjGYoNvzgfG1h/QFXPxgJoIo6JdF1UXbJtrBV+swVQ1elYH3c8dcksK9t1ybbxcayvvnZYyif2PTFTdE6a4DXYI6miqhuC7W+xMHxU9ROxAfGuK7su2TZ+FETXDudQdGhz4KfYsIh46pJZVrbrkm3jbOA9VZ0VfkBEbskCnZMmeB9sGCLyChYsMQGLnlyjqr8UkZrYGL/2riubLtk2BtqLODQFnmB9saO1lKnlKqJLZlnZrktyWScAm0Me4kKPNdIoQVKZonPSB3ewYYjNHnMr0BGYB7yiqgfFoouP1SjjU113pC7ZNjqO46QVmgZjhTx5ikfCokkHAYux2bQ2BduDgLrx1CWzrGzXpdDGJdmo85Q+yTvKwxCRfBF5REQWisg2EdkgItMl9sTorksDG7F5jrcAvVW1QFULsMn+txLM8hNHXTLLynZdqmzsFabbkiU6J11ItYdPt4StenEDNp7ul9i8u22B17CJ5l1XRl0KbIw1j2xcjyWzrGw/li52ZMsxT+mTUm5AuiWOnID9i+A1B1uH0XVl1KXAxnHAr4FGIfsaAb8BxsdTl8yysl2XCTZmks5T+iRvIj6SXWILPiM20f9mALWlumJNIO661Nv4n9hczJNFZIuIbMaWeKuPzWsbT10yy8p2XSbYmEk6J11ItYdPtwScjC3CvBWYQrAUGzbR/F2uK7su2TYGedpja9vmh+3vE29dMsvKdl0m2JhJOk/pkVJuQCYl4EbXxUeXiLKwdUe/wpaMWwn0CzkWa43WcuuSWVa26zLBxkzSeUqflHIDMikB37ouPrpElIUtwJ0fbLcCZgJ3B+9jLS5ebl0yy8p2XSbYmEk6T+mTfKrEMERkfrRDWICB68qoS7aN2BSLOwHUFnbvBYwSW+g7Vt9tRXTJLCvbdZlgYybpnHQh1R4+3RK2OPgp2Py3oakVttak68qoS4GNnwKnhO3LxeaqPRhPXTLLynZdJtiYSTpP6ZNSbkC6JWy1lh5Rjr3hurLrUmBjM6BxlGNnx1OXzLKyXZcJNmaSzlP6JJ+L2HEcx3ESgI+DdRzHcZwE4A7WcRzHcRKAO1jHiYEYU8TWHS3Zd7WIxFrQ23Ecx/tgHac0RKQTtnpJF6AKMBebSefrozhnrqoWxclEx3HSEHewjlMGRORJYBdQC9ihqo+KyM+AO4A84J/AQFUtFpGXgFOBGsAIVX0kOMdq4EWgDzBUVX3JMcfJYnyiCccpGw8Ds4H9QLegVnsFcJaqFgVO9RrgDeB+Vd0sIrnARBEZpaqLgvPsUtWzU/EBHMdJLu5gHacMqOouERkB7FTVfSJyPnAaMFNEwGqrq4Ls14rIzdj/6zigI1DiYEck13LHcVKFO1jHKTvFQQKbqu4VVf1DaAYRaQvcDZyuqltF5HWgekiWXUmx1HGclONRxI5TMcYDV4tIAwARKRCRFsAxwA5gu4g0AS5MoY2O46QQr8E6TgVQ1QUi8jAwXkRygAPAbdiKJ4uAhcByYGrqrHQcJ5V4FLHjOI7jJABvInYcx3GcBOAO1nEcx3ESgDtYx3Ecx0kA7mAdx3EcJwG4g3Ucx3GcBOAO1nEcx3ESgDtYx3Ecx0kA7mAdx3EcJwH8P2EmWcUxgALuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  define color list\n",
    "colors = ['b', 'g', 'r', 'k', 'c', 'm', 'y']\n",
    "##  define index for iterating through color list\n",
    "index = 0\n",
    "##  for each river segment\n",
    "for segment in segments:\n",
    "    ##  subset df for the current river segment\n",
    "    segDF = data.loc[data['RiverSeg'] == segment]\n",
    "    segDF = segDF[segDF[\"lam'\"]>0]\n",
    "\n",
    "    ## sort based on year\n",
    "    segDF = segDF.sort_values('Year')\n",
    "    ## define x,y for plotting\n",
    "    x = segDF[\"Year\"]\n",
    "    y = segDF[\"N1\"]\n",
    "    ## change name of y to Riv Seg for legend\n",
    "    y.name = segment\n",
    "    ##  build graph...\n",
    "    ##  plot segment x vs y\n",
    "    plt.plot(x,y,colors[index])\n",
    "    ##  locate legend\n",
    "    plt.legend(loc=(1.05,0.2))\n",
    "    ##  advance color index\n",
    "    index += 1\n",
    "\n",
    "##  update title\n",
    "plt.title(\"Hill N1, \" + river + \" River Segments\")\n",
    "##  label x axis\n",
    "plt.xlabel('Year')\n",
    "##  label y axis\n",
    "plt.ylabel(\"N1'\")\n",
    "##  force x axis to integer values, increment by 1 year\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "##  rotate year labels 90 degrees\n",
    "plt.xticks(rotation=90)    \n",
    "    \n",
    "##  save figure\n",
    "plt.savefig(output + \"\\\\\" + river + \"_Hill_N1.png\", bbox_inches='tight',dpi=300, size=(2000,2000))\n",
    "##  display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "## add new column\n",
    "data[\"N2\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  for each row calculate Hill N2\n",
    "for i in range(len(data)):\n",
    "    N = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            N += data.iloc[i][x]\n",
    "\n",
    "    lam = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        lam += (data.iloc[i][x]/N) * (data.iloc[i][x]/N)\n",
    "    data.loc[i,'N2'] = 1/lam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>Margalef</th>\n",
       "      <th>SWI_2</th>\n",
       "      <th>SWI_e</th>\n",
       "      <th>SWI_10</th>\n",
       "      <th>lam</th>\n",
       "      <th>1-lam</th>\n",
       "      <th>lam'</th>\n",
       "      <th>1-lam'</th>\n",
       "      <th>N1</th>\n",
       "      <th>N2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>2.87598</td>\n",
       "      <td>0.351927</td>\n",
       "      <td>0.243937</td>\n",
       "      <td>0.10594</td>\n",
       "      <td>0.916313</td>\n",
       "      <td>0.0836871</td>\n",
       "      <td>0.908269</td>\n",
       "      <td>0.091731</td>\n",
       "      <td>1.27626</td>\n",
       "      <td>1.09133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>3.10716</td>\n",
       "      <td>0.490434</td>\n",
       "      <td>0.339943</td>\n",
       "      <td>0.147635</td>\n",
       "      <td>0.870082</td>\n",
       "      <td>0.129918</td>\n",
       "      <td>0.848049</td>\n",
       "      <td>0.151951</td>\n",
       "      <td>1.40487</td>\n",
       "      <td>1.14932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>3.15943</td>\n",
       "      <td>0.461377</td>\n",
       "      <td>0.319802</td>\n",
       "      <td>0.138888</td>\n",
       "      <td>0.88243</td>\n",
       "      <td>0.11757</td>\n",
       "      <td>0.86173</td>\n",
       "      <td>0.13827</td>\n",
       "      <td>1.37686</td>\n",
       "      <td>1.13323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.57169</td>\n",
       "      <td>0.0865042</td>\n",
       "      <td>0.0599602</td>\n",
       "      <td>0.0260404</td>\n",
       "      <td>0.978497</td>\n",
       "      <td>0.0215028</td>\n",
       "      <td>0.97397</td>\n",
       "      <td>0.0260297</td>\n",
       "      <td>1.06179</td>\n",
       "      <td>1.02198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT   ...     Margalef      SWI_2      SWI_e     SWI_10       lam  \\\n",
       "0  0.048077   ...      2.87598   0.351927   0.243937    0.10594  0.916313   \n",
       "1  0.013793   ...      3.10716   0.490434   0.339943   0.147635  0.870082   \n",
       "2  0.019608   ...      3.15943   0.461377   0.319802   0.138888   0.88243   \n",
       "3  0.000000   ...      0.57169  0.0865042  0.0599602  0.0260404  0.978497   \n",
       "4  0.000000   ...            0          0          0          0         1   \n",
       "\n",
       "       1-lam      lam'     1-lam'       N1       N2  \n",
       "0  0.0836871  0.908269   0.091731  1.27626  1.09133  \n",
       "1   0.129918  0.848049   0.151951  1.40487  1.14932  \n",
       "2    0.11757   0.86173    0.13827  1.37686  1.13323  \n",
       "3  0.0215028   0.97397  0.0260297  1.06179  1.02198  \n",
       "4          0         1          0        1        1  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  define color list\n",
    "colors = ['b', 'g', 'r', 'k', 'c', 'm', 'y']\n",
    "##  define index for iterating through color list\n",
    "index = 0\n",
    "##  for each river segment\n",
    "for segment in segments:\n",
    "    ##  subset df for the current river segment\n",
    "    segDF = data.loc[data['RiverSeg'] == segment]\n",
    "    segDF = segDF[segDF[\"lam'\"]>0]\n",
    "\n",
    "    ## sort based on year\n",
    "    segDF = segDF.sort_values('Year')\n",
    "    ## define x,y for plotting\n",
    "    x = segDF[\"Year\"]\n",
    "    y = segDF[\"N2\"]\n",
    "    \n",
    "    ## change name of y to Riv Seg for legend\n",
    "    y.name = segment\n",
    "    ##  build graph...\n",
    "    plt.plot(x,y,colors[index])\n",
    "    ##  locate legend\n",
    "    plt.legend(loc=(1.05,0.2))\n",
    "    ##  advance color index\n",
    "    index += 1\n",
    "\n",
    "##  update title\n",
    "plt.title(\"Hill N2, \" + river + \" River Segments\")\n",
    "##  label x axis\n",
    "plt.xlabel('Year')\n",
    "##  label y axis\n",
    "plt.ylabel(\"N2'\")\n",
    "##  force x axis to integer values, increment by 1 year\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "##  rotate year labels 90 degrees\n",
    "plt.xticks(rotation=90)    \n",
    "    \n",
    "##  save figure\n",
    "plt.savefig(output + \"\\\\\" + river + \"_Hill_N2.png\", bbox_inches='tight',dpi=300, size=(2000,2000))\n",
    "##  display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "## add new column\n",
    "data[\"N_Inf\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "##  for each row calculate Hill N-infinity\n",
    "for i in range(len(data)):\n",
    "    \n",
    "    N = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            N += data.iloc[i][x]\n",
    "    \n",
    "    \n",
    "    array = data.iloc[i][4:last]\n",
    "    data.loc[i,'N_Inf'] = 1/(max(array)/N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>SWI_2</th>\n",
       "      <th>SWI_e</th>\n",
       "      <th>SWI_10</th>\n",
       "      <th>lam</th>\n",
       "      <th>1-lam</th>\n",
       "      <th>lam'</th>\n",
       "      <th>1-lam'</th>\n",
       "      <th>N1</th>\n",
       "      <th>N2</th>\n",
       "      <th>N_Inf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.351927</td>\n",
       "      <td>0.243937</td>\n",
       "      <td>0.10594</td>\n",
       "      <td>0.916313</td>\n",
       "      <td>0.0836871</td>\n",
       "      <td>0.908269</td>\n",
       "      <td>0.091731</td>\n",
       "      <td>1.27626</td>\n",
       "      <td>1.09133</td>\n",
       "      <td>1.04493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>0.490434</td>\n",
       "      <td>0.339943</td>\n",
       "      <td>0.147635</td>\n",
       "      <td>0.870082</td>\n",
       "      <td>0.129918</td>\n",
       "      <td>0.848049</td>\n",
       "      <td>0.151951</td>\n",
       "      <td>1.40487</td>\n",
       "      <td>1.14932</td>\n",
       "      <td>1.07296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>0.461377</td>\n",
       "      <td>0.319802</td>\n",
       "      <td>0.138888</td>\n",
       "      <td>0.88243</td>\n",
       "      <td>0.11757</td>\n",
       "      <td>0.86173</td>\n",
       "      <td>0.13827</td>\n",
       "      <td>1.37686</td>\n",
       "      <td>1.13323</td>\n",
       "      <td>1.06514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0865042</td>\n",
       "      <td>0.0599602</td>\n",
       "      <td>0.0260404</td>\n",
       "      <td>0.978497</td>\n",
       "      <td>0.0215028</td>\n",
       "      <td>0.97397</td>\n",
       "      <td>0.0260297</td>\n",
       "      <td>1.06179</td>\n",
       "      <td>1.02198</td>\n",
       "      <td>1.01099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT   ...         SWI_2      SWI_e     SWI_10       lam      1-lam  \\\n",
       "0  0.048077   ...      0.351927   0.243937    0.10594  0.916313  0.0836871   \n",
       "1  0.013793   ...      0.490434   0.339943   0.147635  0.870082   0.129918   \n",
       "2  0.019608   ...      0.461377   0.319802   0.138888   0.88243    0.11757   \n",
       "3  0.000000   ...     0.0865042  0.0599602  0.0260404  0.978497  0.0215028   \n",
       "4  0.000000   ...             0          0          0         1          0   \n",
       "\n",
       "       lam'     1-lam'       N1       N2    N_Inf  \n",
       "0  0.908269   0.091731  1.27626  1.09133  1.04493  \n",
       "1  0.848049   0.151951  1.40487  1.14932  1.07296  \n",
       "2   0.86173    0.13827  1.37686  1.13323  1.06514  \n",
       "3   0.97397  0.0260297  1.06179  1.02198  1.01099  \n",
       "4         1          0        1        1        1  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  define color list\n",
    "colors = ['b', 'g', 'r', 'k', 'c', 'm', 'y']\n",
    "##  define index for iterating through color list\n",
    "index = 0\n",
    "##  for each river segment\n",
    "for segment in segments:\n",
    "    ##  subset df to non-zero values for the current river segment\n",
    "    segDF = data.loc[data['RiverSeg'] == segment]\n",
    "    segDF = segDF[segDF[\"lam'\"]>0]\n",
    "\n",
    "    ## sort based on year\n",
    "    segDF = segDF.sort_values('Year')\n",
    "    ## define x,y for plotting\n",
    "    x = segDF[\"Year\"]\n",
    "    y = segDF[\"N_Inf\"]\n",
    "    ## change name of y to Riv Seg for legend\n",
    "    y.name = segment\n",
    "    ##  build graph...\n",
    "    ##  plot segment x vs y\n",
    "    plt.plot(x,y,colors[index])\n",
    "    ##  locate legend\n",
    "    plt.legend(loc=(1.05,0.2))\n",
    "    ##  advance color index\n",
    "    index += 1\n",
    "\n",
    "##  update title\n",
    "plt.title(\"Hill N_Inf, \" + river + \" River Segments\")\n",
    "##  label x axis\n",
    "plt.xlabel('Year')\n",
    "##  label y axis\n",
    "plt.ylabel(\"N_Inf'\")\n",
    "##  force x axis to integer values, increment by 1 year\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "##  rotate year labels 90 degrees\n",
    "plt.xticks(rotation=90)    \n",
    "\n",
    "##  save figure\n",
    "plt.savefig(output + \"\\\\\" + river + \"_Hill_N_Inf.png\", bbox_inches='tight',dpi=300, size=(2000,2000))\n",
    "##  display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "## add new column\n",
    "data[\"N10\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  for each row calculate Hill N10\n",
    "for i in range(len(data)):\n",
    "    swi = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            swi += -(data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])) * math.log((data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])))\n",
    "    data.loc[i,'N10'] = math.exp(swi)/len(data.iloc[i][4:last][data.iloc[i][4:last]>0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>SWI_e</th>\n",
       "      <th>SWI_10</th>\n",
       "      <th>lam</th>\n",
       "      <th>1-lam</th>\n",
       "      <th>lam'</th>\n",
       "      <th>1-lam'</th>\n",
       "      <th>N1</th>\n",
       "      <th>N2</th>\n",
       "      <th>N_Inf</th>\n",
       "      <th>N10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.243937</td>\n",
       "      <td>0.10594</td>\n",
       "      <td>0.916313</td>\n",
       "      <td>0.0836871</td>\n",
       "      <td>0.908269</td>\n",
       "      <td>0.091731</td>\n",
       "      <td>1.27626</td>\n",
       "      <td>1.09133</td>\n",
       "      <td>1.04493</td>\n",
       "      <td>0.159533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>0.339943</td>\n",
       "      <td>0.147635</td>\n",
       "      <td>0.870082</td>\n",
       "      <td>0.129918</td>\n",
       "      <td>0.848049</td>\n",
       "      <td>0.151951</td>\n",
       "      <td>1.40487</td>\n",
       "      <td>1.14932</td>\n",
       "      <td>1.07296</td>\n",
       "      <td>0.200695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>0.319802</td>\n",
       "      <td>0.138888</td>\n",
       "      <td>0.88243</td>\n",
       "      <td>0.11757</td>\n",
       "      <td>0.86173</td>\n",
       "      <td>0.13827</td>\n",
       "      <td>1.37686</td>\n",
       "      <td>1.13323</td>\n",
       "      <td>1.06514</td>\n",
       "      <td>0.196694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0599602</td>\n",
       "      <td>0.0260404</td>\n",
       "      <td>0.978497</td>\n",
       "      <td>0.0215028</td>\n",
       "      <td>0.97397</td>\n",
       "      <td>0.0260297</td>\n",
       "      <td>1.06179</td>\n",
       "      <td>1.02198</td>\n",
       "      <td>1.01099</td>\n",
       "      <td>0.530897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT    ...         SWI_e     SWI_10       lam      1-lam      lam'  \\\n",
       "0  0.048077    ...      0.243937    0.10594  0.916313  0.0836871  0.908269   \n",
       "1  0.013793    ...      0.339943   0.147635  0.870082   0.129918  0.848049   \n",
       "2  0.019608    ...      0.319802   0.138888   0.88243    0.11757   0.86173   \n",
       "3  0.000000    ...     0.0599602  0.0260404  0.978497  0.0215028   0.97397   \n",
       "4  0.000000    ...             0          0         1          0         1   \n",
       "\n",
       "      1-lam'       N1       N2    N_Inf       N10  \n",
       "0   0.091731  1.27626  1.09133  1.04493  0.159533  \n",
       "1   0.151951  1.40487  1.14932  1.07296  0.200695  \n",
       "2    0.13827  1.37686  1.13323  1.06514  0.196694  \n",
       "3  0.0260297  1.06179  1.02198  1.01099  0.530897  \n",
       "4          0        1        1        1         1  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  define color list\n",
    "colors = ['b', 'g', 'r', 'k', 'c', 'm', 'y']\n",
    "##  define index for iterating through color list\n",
    "index = 0\n",
    "\n",
    "##  for each river segment\n",
    "for segment in segments:\n",
    "    ##  subset df to non-zero values for the current river segment\n",
    "    segDF = data.loc[data['RiverSeg'] == segment]\n",
    "    segDF = segDF[segDF[\"lam'\"]>0]\n",
    "\n",
    "    ## sort based on year\n",
    "    segDF = segDF.sort_values('Year')\n",
    "    ## define x,y for plotting\n",
    "    x = segDF[\"Year\"]\n",
    "    y = segDF[\"N10\"]\n",
    "    ## change name of y to Riv Seg for legend\n",
    "    y.name = segment\n",
    "    ##  build graph...\n",
    "    plt.plot(x,y,colors[index])\n",
    "    ##  locate legend\n",
    "    plt.legend(loc=(1.05,0.2))\n",
    "    ##  advance color index\n",
    "    index += 1\n",
    "\n",
    "##  update title\n",
    "plt.title(\"Hill N10, \" + river + \" River Segments\")\n",
    "##  label x axis\n",
    "plt.xlabel('Year')\n",
    "##  label y axis\n",
    "plt.ylabel(\"N10\")\n",
    "##  force x axis to integer values, increment by 1 year\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "##  rotate year labels 90 degrees\n",
    "plt.xticks(rotation=90)    \n",
    "    \n",
    "##  save figure\n",
    "plt.savefig(output + \"\\\\\" + river + \"_Hill_N10.png\", bbox_inches='tight',dpi=300, size=(2000,2000))\n",
    "##  display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "## add new column\n",
    "data[\"N10'\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  for each row calculate Hill N10 prime \n",
    "for i in range(len(data)):\n",
    "    swi = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            swi += -(data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])) * math.log((data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])))\n",
    "    try:\n",
    "        data.loc[i,\"N10'\"] = (math.exp(swi)-1)/(len(data.iloc[i][4:last][data.iloc[i][4:last]>0])-1)\n",
    "    except:\n",
    "        data.loc[i,\"N10'\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>SWI_10</th>\n",
       "      <th>lam</th>\n",
       "      <th>1-lam</th>\n",
       "      <th>lam'</th>\n",
       "      <th>1-lam'</th>\n",
       "      <th>N1</th>\n",
       "      <th>N2</th>\n",
       "      <th>N_Inf</th>\n",
       "      <th>N10</th>\n",
       "      <th>N10'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10594</td>\n",
       "      <td>0.916313</td>\n",
       "      <td>0.0836871</td>\n",
       "      <td>0.908269</td>\n",
       "      <td>0.091731</td>\n",
       "      <td>1.27626</td>\n",
       "      <td>1.09133</td>\n",
       "      <td>1.04493</td>\n",
       "      <td>0.159533</td>\n",
       "      <td>0.0394663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>0.147635</td>\n",
       "      <td>0.870082</td>\n",
       "      <td>0.129918</td>\n",
       "      <td>0.848049</td>\n",
       "      <td>0.151951</td>\n",
       "      <td>1.40487</td>\n",
       "      <td>1.14932</td>\n",
       "      <td>1.07296</td>\n",
       "      <td>0.200695</td>\n",
       "      <td>0.0674778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>0.138888</td>\n",
       "      <td>0.88243</td>\n",
       "      <td>0.11757</td>\n",
       "      <td>0.86173</td>\n",
       "      <td>0.13827</td>\n",
       "      <td>1.37686</td>\n",
       "      <td>1.13323</td>\n",
       "      <td>1.06514</td>\n",
       "      <td>0.196694</td>\n",
       "      <td>0.0628092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0260404</td>\n",
       "      <td>0.978497</td>\n",
       "      <td>0.0215028</td>\n",
       "      <td>0.97397</td>\n",
       "      <td>0.0260297</td>\n",
       "      <td>1.06179</td>\n",
       "      <td>1.02198</td>\n",
       "      <td>1.01099</td>\n",
       "      <td>0.530897</td>\n",
       "      <td>0.0617942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT    ...         SWI_10       lam      1-lam      lam'     1-lam'  \\\n",
       "0  0.048077    ...        0.10594  0.916313  0.0836871  0.908269   0.091731   \n",
       "1  0.013793    ...       0.147635  0.870082   0.129918  0.848049   0.151951   \n",
       "2  0.019608    ...       0.138888   0.88243    0.11757   0.86173    0.13827   \n",
       "3  0.000000    ...      0.0260404  0.978497  0.0215028   0.97397  0.0260297   \n",
       "4  0.000000    ...              0         1          0         1          0   \n",
       "\n",
       "        N1       N2    N_Inf       N10       N10'  \n",
       "0  1.27626  1.09133  1.04493  0.159533  0.0394663  \n",
       "1  1.40487  1.14932  1.07296  0.200695  0.0674778  \n",
       "2  1.37686  1.13323  1.06514  0.196694  0.0628092  \n",
       "3  1.06179  1.02198  1.01099  0.530897  0.0617942  \n",
       "4        1        1        1         1          0  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  define color list\n",
    "colors = ['b', 'g', 'r', 'k', 'c', 'm', 'y']\n",
    "##  define index for iterating through color list\n",
    "index = 0\n",
    "##  for each river segment\n",
    "for segment in segments:\n",
    "    ##  subset df to non-zero values for the current river segment\n",
    "    segDF = data.loc[data['RiverSeg'] == segment]\n",
    "    segDF = segDF[segDF[\"lam'\"]>0]\n",
    "\n",
    "    ## sort based on year\n",
    "    segDF = segDF.sort_values('Year')\n",
    "    ## define x,y for plotting\n",
    "    x = segDF[\"Year\"]\n",
    "    y = segDF[\"N10'\"]\n",
    "    ## change name of y to Riv Seg for legend\n",
    "    y.name = segment\n",
    "    ##  build graph...\n",
    "    ##  plot segment x vs y\n",
    "    plt.plot(x,y,colors[index])\n",
    "    ##  locate legend\n",
    "    plt.legend(loc=(1.05,0.2))\n",
    "    ##  advance color index\n",
    "    index += 1\n",
    "\n",
    "##  update title\n",
    "plt.title(\"Hill N10', \" + river + \" River Segments\")\n",
    "##  label x axis\n",
    "plt.xlabel('Year')\n",
    "##  label y axis\n",
    "plt.ylabel(\"N10'\")\n",
    "##  force x axis to integer values, increment by 1 year\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "##  rotate year labels 90 degrees\n",
    "plt.xticks(rotation=90)    \n",
    "    \n",
    "##  save figure\n",
    "plt.savefig(output + \"\\\\\" + river + \"_Hill_N10'.png\", bbox_inches='tight',dpi=300, size=(2000,2000))\n",
    "##  display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "## add new column\n",
    "data[\"N21\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  for each row calculate Hill N21\n",
    "for i in range(len(data)):\n",
    "    N = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            N += data.iloc[i][x]\n",
    "    #N2\n",
    "    lam = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        lam += (data.iloc[i][x]/N) * (data.iloc[i][x]/N)\n",
    "    N2 = 1/lam\n",
    "        \n",
    "    swi = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            swi += -(data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])) * math.log((data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])))\n",
    "    N1 = math.exp(swi)\n",
    "    \n",
    "    data.loc[i,\"N21\"] = N2/N1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>lam</th>\n",
       "      <th>1-lam</th>\n",
       "      <th>lam'</th>\n",
       "      <th>1-lam'</th>\n",
       "      <th>N1</th>\n",
       "      <th>N2</th>\n",
       "      <th>N_Inf</th>\n",
       "      <th>N10</th>\n",
       "      <th>N10'</th>\n",
       "      <th>N21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.916313</td>\n",
       "      <td>0.0836871</td>\n",
       "      <td>0.908269</td>\n",
       "      <td>0.091731</td>\n",
       "      <td>1.27626</td>\n",
       "      <td>1.09133</td>\n",
       "      <td>1.04493</td>\n",
       "      <td>0.159533</td>\n",
       "      <td>0.0394663</td>\n",
       "      <td>0.855098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>0.870082</td>\n",
       "      <td>0.129918</td>\n",
       "      <td>0.848049</td>\n",
       "      <td>0.151951</td>\n",
       "      <td>1.40487</td>\n",
       "      <td>1.14932</td>\n",
       "      <td>1.07296</td>\n",
       "      <td>0.200695</td>\n",
       "      <td>0.0674778</td>\n",
       "      <td>0.818097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>0.88243</td>\n",
       "      <td>0.11757</td>\n",
       "      <td>0.86173</td>\n",
       "      <td>0.13827</td>\n",
       "      <td>1.37686</td>\n",
       "      <td>1.13323</td>\n",
       "      <td>1.06514</td>\n",
       "      <td>0.196694</td>\n",
       "      <td>0.0628092</td>\n",
       "      <td>0.82306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.978497</td>\n",
       "      <td>0.0215028</td>\n",
       "      <td>0.97397</td>\n",
       "      <td>0.0260297</td>\n",
       "      <td>1.06179</td>\n",
       "      <td>1.02198</td>\n",
       "      <td>1.01099</td>\n",
       "      <td>0.530897</td>\n",
       "      <td>0.0617942</td>\n",
       "      <td>0.962499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT    ...          lam      1-lam      lam'     1-lam'       N1  \\\n",
       "0  0.048077    ...     0.916313  0.0836871  0.908269   0.091731  1.27626   \n",
       "1  0.013793    ...     0.870082   0.129918  0.848049   0.151951  1.40487   \n",
       "2  0.019608    ...      0.88243    0.11757   0.86173    0.13827  1.37686   \n",
       "3  0.000000    ...     0.978497  0.0215028   0.97397  0.0260297  1.06179   \n",
       "4  0.000000    ...            1          0         1          0        1   \n",
       "\n",
       "        N2    N_Inf       N10       N10'       N21  \n",
       "0  1.09133  1.04493  0.159533  0.0394663  0.855098  \n",
       "1  1.14932  1.07296  0.200695  0.0674778  0.818097  \n",
       "2  1.13323  1.06514  0.196694  0.0628092   0.82306  \n",
       "3  1.02198  1.01099  0.530897  0.0617942  0.962499  \n",
       "4        1        1         1          0         1  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  define color list\n",
    "colors = ['b', 'g', 'r', 'k', 'c', 'm', 'y']\n",
    "##  define index for iterating through color list\n",
    "index = 0\n",
    "##  for each river segment\n",
    "for segment in segments:\n",
    "    ##  subset df to non-zero values for the current river segment\n",
    "    segDF = data.loc[data['RiverSeg'] == segment]\n",
    "    segDF = segDF[segDF[\"lam'\"]>0]\n",
    "\n",
    "    ## sort based on year\n",
    "    segDF = segDF.sort_values('Year')\n",
    "    ## define x,y for plotting\n",
    "    x = segDF[\"Year\"]\n",
    "    y = segDF[\"N21\"]\n",
    "    ## change name of y to Riv Seg for legend\n",
    "    y.name = segment\n",
    "    ##  build graph...\n",
    "    ##  plot segment x vs y\n",
    "    plt.plot(x,y,colors[index])\n",
    "    ##  locate legend\n",
    "    plt.legend(loc=(1.05,0.2))\n",
    "    ##  advance color index\n",
    "    index += 1\n",
    "\n",
    "##  update title\n",
    "plt.title(\"Hill N21, \" + river + \" River Segments\")\n",
    "##  label x axis\n",
    "plt.xlabel('Year')\n",
    "##  label y axis\n",
    "plt.ylabel(\"N21\")\n",
    "##  force x axis to integer values, increment by 1 year\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "##  rotate year labels 90 degrees\n",
    "plt.xticks(rotation=90)    \n",
    "    \n",
    "##  save figure\n",
    "plt.savefig(output + \"\\\\\" + river + \"_Hill_N21.png\", bbox_inches='tight',dpi=300, size=(2000,2000))\n",
    "##  display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "## add new column\n",
    "data[\"N21'\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda2\\envs\\python36\\lib\\site-packages\\ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    }
   ],
   "source": [
    "##  for each row calculate Hill N21 Prime\n",
    "for i in range(len(data)):\n",
    "\n",
    "    N = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            N += data.iloc[i][x]\n",
    "    #N2\n",
    "    lam = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        lam += (data.iloc[i][x]/N) * (data.iloc[i][x]/N)\n",
    "    N2 = 1/lam\n",
    "\n",
    "    swi = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            swi += -(data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])) * math.log((data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])))\n",
    "    N1 = math.exp(swi)\n",
    "\n",
    "\n",
    "    data.loc[i,\"N21'\"] = (N2-1)/(N1-1)\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>1-lam</th>\n",
       "      <th>lam'</th>\n",
       "      <th>1-lam'</th>\n",
       "      <th>N1</th>\n",
       "      <th>N2</th>\n",
       "      <th>N_Inf</th>\n",
       "      <th>N10</th>\n",
       "      <th>N10'</th>\n",
       "      <th>N21</th>\n",
       "      <th>N21'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0836871</td>\n",
       "      <td>0.908269</td>\n",
       "      <td>0.091731</td>\n",
       "      <td>1.27626</td>\n",
       "      <td>1.09133</td>\n",
       "      <td>1.04493</td>\n",
       "      <td>0.159533</td>\n",
       "      <td>0.0394663</td>\n",
       "      <td>0.855098</td>\n",
       "      <td>0.330591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>0.129918</td>\n",
       "      <td>0.848049</td>\n",
       "      <td>0.151951</td>\n",
       "      <td>1.40487</td>\n",
       "      <td>1.14932</td>\n",
       "      <td>1.07296</td>\n",
       "      <td>0.200695</td>\n",
       "      <td>0.0674778</td>\n",
       "      <td>0.818097</td>\n",
       "      <td>0.368805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>0.11757</td>\n",
       "      <td>0.86173</td>\n",
       "      <td>0.13827</td>\n",
       "      <td>1.37686</td>\n",
       "      <td>1.13323</td>\n",
       "      <td>1.06514</td>\n",
       "      <td>0.196694</td>\n",
       "      <td>0.0628092</td>\n",
       "      <td>0.82306</td>\n",
       "      <td>0.353543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0215028</td>\n",
       "      <td>0.97397</td>\n",
       "      <td>0.0260297</td>\n",
       "      <td>1.06179</td>\n",
       "      <td>1.02198</td>\n",
       "      <td>1.01099</td>\n",
       "      <td>0.530897</td>\n",
       "      <td>0.0617942</td>\n",
       "      <td>0.962499</td>\n",
       "      <td>0.355622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT    ...         1-lam      lam'     1-lam'       N1       N2  \\\n",
       "0  0.048077    ...     0.0836871  0.908269   0.091731  1.27626  1.09133   \n",
       "1  0.013793    ...      0.129918  0.848049   0.151951  1.40487  1.14932   \n",
       "2  0.019608    ...       0.11757   0.86173    0.13827  1.37686  1.13323   \n",
       "3  0.000000    ...     0.0215028   0.97397  0.0260297  1.06179  1.02198   \n",
       "4  0.000000    ...             0         1          0        1        1   \n",
       "\n",
       "     N_Inf       N10       N10'       N21      N21'  \n",
       "0  1.04493  0.159533  0.0394663  0.855098  0.330591  \n",
       "1  1.07296  0.200695  0.0674778  0.818097  0.368805  \n",
       "2  1.06514  0.196694  0.0628092   0.82306  0.353543  \n",
       "3  1.01099  0.530897  0.0617942  0.962499  0.355622  \n",
       "4        1         1          0         1       NaN  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  define color list\n",
    "colors = ['b', 'g', 'r', 'k', 'c', 'm', 'y']\n",
    "##  define index for iterating through color list\n",
    "index = 0\n",
    "##  for each river segment\n",
    "for segment in segments:\n",
    "    ##  subset df to non-zero values for the current river segment\n",
    "    segDF = data.loc[data['RiverSeg'] == segment]\n",
    "    segDF = segDF[segDF[\"lam'\"]>0]\n",
    "\n",
    "    ## sort based on year\n",
    "    segDF = segDF.sort_values('Year')\n",
    "    ## define x,y for plotting\n",
    "    x = segDF[\"Year\"]\n",
    "    y = segDF[\"N21'\"]\n",
    "    ## change name of y to Riv Seg for legend\n",
    "    y.name = segment\n",
    "    ##  build graph...\n",
    "    ##  plot segment x vs y\n",
    "    plt.plot(x,y,colors[index])\n",
    "    ##  locate legend\n",
    "    plt.legend(loc=(1.05,0.2))\n",
    "    ##  advance color index\n",
    "    index += 1\n",
    "\n",
    "##  update title\n",
    "plt.title(\"Hill N21', \" + river + \" River Segments\")\n",
    "##  label x axis\n",
    "plt.xlabel('Year')\n",
    "##  label y axis\n",
    "plt.ylabel(\"N21'\")\n",
    "##  force x axis to integer values, increment by 1 year\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "##  rotate year labels 90 degrees\n",
    "plt.xticks(rotation=90)\n",
    "    \n",
    "##  save figure\n",
    "plt.savefig(output + \"\\\\\" + river + \"_Hill_N21'.png\", bbox_inches='tight',dpi=300, size=(2000,2000))\n",
    "##  display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "## add new column\n",
    "data[\"Pielou\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda2\\envs\\python36\\lib\\site-packages\\ipykernel_launcher.py:17: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    }
   ],
   "source": [
    "##  for each row calculate Pielou's Evenness Index \n",
    "for i in range(len(data)):\n",
    "\n",
    "    count = 0.0\n",
    "    hmax = 0.0\n",
    "    \n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            count += 1\n",
    "\n",
    "    swi = 0.0\n",
    "    for x in range(len(data.iloc[0]))[4:last]:\n",
    "        if data.iloc[i][x] > 0:\n",
    "            swi += -(data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])) * math.log((data.iloc[i][x]/sum(data.iloc[i][4:last][data.iloc[i][4:last]>0])))\n",
    "\n",
    "          \n",
    "    data.loc[i,'Pielou'] = swi/math.log(count)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>River</th>\n",
       "      <th>RiverSeg</th>\n",
       "      <th>Year</th>\n",
       "      <th>BHSK</th>\n",
       "      <th>BKBH</th>\n",
       "      <th>BKCP</th>\n",
       "      <th>BKTT</th>\n",
       "      <th>BLGL</th>\n",
       "      <th>BNTT</th>\n",
       "      <th>...</th>\n",
       "      <th>lam'</th>\n",
       "      <th>1-lam'</th>\n",
       "      <th>N1</th>\n",
       "      <th>N2</th>\n",
       "      <th>N_Inf</th>\n",
       "      <th>N10</th>\n",
       "      <th>N10'</th>\n",
       "      <th>N21</th>\n",
       "      <th>N21'</th>\n",
       "      <th>Pielou</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.908269</td>\n",
       "      <td>0.091731</td>\n",
       "      <td>1.27626</td>\n",
       "      <td>1.09133</td>\n",
       "      <td>1.04493</td>\n",
       "      <td>0.159533</td>\n",
       "      <td>0.0394663</td>\n",
       "      <td>0.855098</td>\n",
       "      <td>0.330591</td>\n",
       "      <td>0.117309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>0.848049</td>\n",
       "      <td>0.151951</td>\n",
       "      <td>1.40487</td>\n",
       "      <td>1.14932</td>\n",
       "      <td>1.07296</td>\n",
       "      <td>0.200695</td>\n",
       "      <td>0.0674778</td>\n",
       "      <td>0.818097</td>\n",
       "      <td>0.368805</td>\n",
       "      <td>0.174696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>...</td>\n",
       "      <td>0.86173</td>\n",
       "      <td>0.13827</td>\n",
       "      <td>1.37686</td>\n",
       "      <td>1.13323</td>\n",
       "      <td>1.06514</td>\n",
       "      <td>0.196694</td>\n",
       "      <td>0.0628092</td>\n",
       "      <td>0.82306</td>\n",
       "      <td>0.353543</td>\n",
       "      <td>0.164346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.97397</td>\n",
       "      <td>0.0260297</td>\n",
       "      <td>1.06179</td>\n",
       "      <td>1.02198</td>\n",
       "      <td>1.01099</td>\n",
       "      <td>0.530897</td>\n",
       "      <td>0.0617942</td>\n",
       "      <td>0.962499</td>\n",
       "      <td>0.355622</td>\n",
       "      <td>0.0865042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Upper</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     River RiverSeg  Year  BHSK      BKBH  BKCP  BKTT  BLGL  \\\n",
       "0         1  Colorado    Upper  1991     0  0.009615     0     0     0   \n",
       "1         2  Colorado    Upper  1992     0  0.000000     0     0     0   \n",
       "2         3  Colorado    Upper  1993     0  0.000000     0     0     0   \n",
       "3         4  Colorado    Upper  1994     0  0.000000     0     0     0   \n",
       "4         5  Colorado    Upper  1996     0  0.000000     0     0     0   \n",
       "\n",
       "       BNTT    ...          lam'     1-lam'       N1       N2    N_Inf  \\\n",
       "0  0.048077    ...      0.908269   0.091731  1.27626  1.09133  1.04493   \n",
       "1  0.013793    ...      0.848049   0.151951  1.40487  1.14932  1.07296   \n",
       "2  0.019608    ...       0.86173    0.13827  1.37686  1.13323  1.06514   \n",
       "3  0.000000    ...       0.97397  0.0260297  1.06179  1.02198  1.01099   \n",
       "4  0.000000    ...             1          0        1        1        1   \n",
       "\n",
       "        N10       N10'       N21      N21'     Pielou  \n",
       "0  0.159533  0.0394663  0.855098  0.330591   0.117309  \n",
       "1  0.200695  0.0674778  0.818097  0.368805   0.174696  \n",
       "2  0.196694  0.0628092   0.82306  0.353543   0.164346  \n",
       "3  0.530897  0.0617942  0.962499  0.355622  0.0865042  \n",
       "4         1          0         1       NaN        NaN  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display first 5 rows of data frame with new index\n",
    "data.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  define color list\n",
    "colors = ['b', 'g', 'r', 'k', 'c', 'm', 'y']\n",
    "##  define index for iterating through color list\n",
    "index = 0\n",
    "##  for each river segment\n",
    "for segment in segments:\n",
    "    ##  subset df to non-zero values for the current river segment\n",
    "    segDF = data.loc[data['RiverSeg'] == segment]\n",
    "    segDF = segDF[segDF[\"lam'\"]>0]\n",
    "\n",
    "    ## sort based on year\n",
    "    segDF = segDF.sort_values('Year')\n",
    "    ## define x,y for plotting\n",
    "    x = segDF[\"Year\"]\n",
    "    y = segDF[\"Pielou\"]\n",
    "    ## change name of y to Riv Seg for legend\n",
    "    y.name = segment\n",
    "    ##  build graph...\n",
    "    ##  plot segment x vs y\n",
    "    plt.plot(x,y,colors[index])\n",
    "    ##  locate legend\n",
    "    plt.legend(loc=(1.05,0.2))\n",
    "    ##  advance color index\n",
    "    index += 1\n",
    "\n",
    "##  update title\n",
    "plt.title(\"Pielou, \" + river + \" River Segments\")\n",
    "##  label x axis\n",
    "plt.xlabel('Year')\n",
    "##  label y axis\n",
    "plt.ylabel(\"Pielou\")\n",
    "##  force x axis to integer values, increment by 1 year\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "##  rotate year labels 90 degrees\n",
    "plt.xticks(rotation=90)    \n",
    "    \n",
    "##  save figure\n",
    "plt.savefig(output + \"\\\\\" + river + \"_Pielou.png\", bbox_inches='tight',dpi=300, size=(2000,2000))\n",
    "##  display figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  save dataframe with biodiversity indices to csv\n",
    "data.to_csv(output + \"\\\\\" + river + \"_biodiv.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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